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Robotic Navigation - Experience Gained with RADAR Martin Adams Dept. Electrical Engineering, AMTC Universidad de Chile ([email protected]) 2 nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

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Page 1: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Robotic Navigation - Experience Gained with RADAR

Martin Adams

Dept. Electrical Engineering, AMTC Universidad de Chile ([email protected])

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 2: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

1. Mapping, Tracking & SLAM at U. Chile. 2. Sensor Measurement & Detections:

• Landmark Existence and Spatial Uncertainty.

3. Simultaneous Localisation & Map Building (SLAM).

• A Random Finite Set (RFS) Approach.

• Set Based Likelihood Formulation.

• RFS SLAM – A generalization of Random Vector SLAM.

• RFS versus Vector Based SLAM – Results.

4. Future Work in RFS based Mapping/SLAM.

Presentation Outline Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 3: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

1. Mapping, Tracking & SLAM at U. Chile. 2. Sensor Measurement & Detections:

• Landmark Existence and Spatial Uncertainty.

3. Simultaneous Localisation & Map Building (SLAM).

• A Random Finite Set (RFS) Approach.

• Set Based Likelihood Formulation.

• RFS SLAM – A generalization of Random Vector SLAM.

• RFS versus Vector Based SLAM – Results.

4. Future Work in RFS based Mapping/SLAM.

Presentation Outline Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 4: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Mapping El Teniente’s Esmeralda Mine. - VIDEO

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Mapping in Mines/Rugged Terrains

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 5: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Individual LADAR scans

Registered LADAR scans from 45 individual scans

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Mapping in Mines/Rugged Terrains

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 6: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Chilbolton 3GHz range/Doppler radar for SSA

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Space Debris Tracking Detecting & Tracking space debris between 1 and 10 cubic cm

based on optical & radio telescopic data

Vicuña (Chile) NEO Space telescope (U. Chile + U. Serena)

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 7: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Space Debris Tracking Goals: Provide tracking SW framework robust to significant

false alarms & radar/visual blind zones

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 8: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

SLAM in outdoor/mining environments Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 9: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

• Improved estimation and incorporation of detection statistics & measurement likelihoods in RFS based SLAM

SLAM in outdoor/mining environments Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 10: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

1. Mapping, Tracking & SLAM at U. Chile. 2. Sensor Measurement & Detections:

• Landmark Existence and Spatial Uncertainty.

3. Simultaneous Localisation & Map Building (SLAM).

• A Random Finite Set (RFS) Approach.

• Set Based Likelihood Formulation.

• RFS SLAM – A generalization of Random Vector SLAM.

• RFS versus Vector Based SLAM – Results.

4. Future Work in RFS based Mapping/SLAM.

Presentation Outline Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 11: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Acumine Radar 360 deg. scanning unit, 94GHz FMCW

Sick LD-LRS1000 Scanning LRF

Microsoft Kinect camera system

Video El Teniente

Clearpath Robotic Skid Steer Platform

Sensing the Environment Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 12: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

The random nature of detections

Sensing the Environment: Detection Errors

Radar

LRF + Line RANSAC

Visual SURF Features

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 13: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Feature absence & presence statistics

Sensing the Environment: Detection Statistics

Radar

LRF + Line RANSAC

Visual SURF Features

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 14: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

What’s in a Measurement?

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 15: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

What’s in a Measurement?

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 16: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

What’s in a Measurement?

Robotic Interpretation:

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 17: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

What’s in a Measurement?

Robotic Interpretation:

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 18: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

What’s in a Measurement?

Robotic Interpretation:

Radar Interpretation:

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 19: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

What’s in a Measurement?

Robotic Interpretation:

Radar Interpretation:

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 20: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

What’s in a Measurement?

• In reality – Probability of Detection less than unity, but may not be known.

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 21: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

What’s in a Measurement?

• In reality – Probability of Detection less than unity, but may not be known. • However, landmark/feature measurements in SLAM result from a feature detection algorithm.

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 22: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

What’s in a Measurement?

• In reality – Probability of Detection less than unity, but may not be known. • However, landmark/feature measurements in SLAM result from a feature detection algorithm. • Principled algorithms provide estimates of , or they can be estimated a-priori (e.g. RANSAC).

faD PP and

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 23: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

What’s in a Measurement?

• In reality – Probability of Detection less than unity, but may not be known. • However, landmark/feature measurements in SLAM result from a feature detection algorithm. • Principled algorithms provide estimates of , or they can be estimated a-priori (e.g. RANSAC).

• Ideal scenario: Represent all detection hypotheses in terms of their:

faD PP and

fai

kDki

k PrPr and )( ,)( i2 , σ

(i.e. range, spatial uncertainty, detection uncertainty and false alarm probability).

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 24: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Wider beam width Foliage penetration

The Importance of Missed Detections

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 25: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Radar detections registered to ground truth location.

The Importance of False Alarms

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 26: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Radar Based Projects: A*Star - Radar vs. Ladar

Video: Raw_Data_Display.avi

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 27: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Sample of NTU University Campus Dataset

Radar vs. Ladar

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 28: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Laser Based Grid Map Radar Based Grid Map

Note the rich data output due to the multi-target detection capabilities of the radar, to those of the laser.

Radar vs. Ladar

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 29: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

1. Mapping, Tracking & SLAM at U. Chile. 2. Sensor Measurement & Detections:

• Landmark Existence and Spatial Uncertainty.

3. Simultaneous Localisation & Map Building (SLAM).

• A Random Finite Set (RFS) Approach.

• Set Based Likelihood Formulation.

• RFS SLAM – A generalization of Random Vector SLAM.

• RFS versus Vector Based SLAM – Results.

4. Future Work in RFS based Mapping/SLAM.

Presentation Outline Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 30: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

• In an unknown environment – robot & feature positions must be estimated simultaneously - SLAM.

• SLAM is a probabilistic algorithm

1 : 1 :

1 :

1 :

p( , | , )State of the robot at time Map of the environment

Sensor inputs from time 1 to Control inputs from time 1 to

t t t

t

t

t

x m z ux tmz tu t

====

SLAM Fundamentals

• Update distribution estimate with Bayes theorem.

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 31: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

• Estimated map vector depends on vehicle trajectory ?

• RFS makes more sense as order of features cannot/should

not be significant [Mullane, Adams 2009].

4 3 2 1 5 7 6

1

2

3

6 7 5 4 3 2 1

1 2 3 4 5 6 7

Given

Given

Given

M = [m ,m ,

X :

X :

X :M = [m

M = [m

,m

m ,m ,m ,m ,

,m ,m

,m ,m ,

,m

m

,

,m

m

,m ,m

m ]

,m

]

]

A Random Finite Set (RFS) Approach [Mullane, Vo, Adams ‘09]

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 32: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

?

1 2 3 4 5 6 7

1 2 3 4 5 6 7

Z = [z , z , z , z , z , z , z ]

M = [m ,m ,m ,m ,m ,m ,m ]

Untangle:

A Random Finite Set (RFS) Approach Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 33: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

1 2 3 4 5 6 7

1 2 3 4 5 6 7

Z = [z , z , z , z , z , z , z ]

M = [m ,m ,m ,m ,m ,m ,m ]

Untangle:

A Random Finite Set (RFS) Approach Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 34: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

1 2 3 4 5 6 7

1 2 3 4 5 6 7

Z = [z , z , z , z , z , z , z ]

M = [m ,m ,m ,m ,m ,m ,m ]

Untangle:

Current vector formulations require data association (DA) prior to Bayesian update: Why? Features & measurements rigidly ordered in vector-valued map state. RFS approach does not require DA. Why? Features & measurements are finite valued sets. No distinct order assumed.

A Random Finite Set (RFS) Approach Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 35: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

What is a RFS Measurement? Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 36: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

Q: Why do we even care about error in the number of landmarks?

Catastrophic consequences in applications such as search & rescue, obstacle avoidance, UAV missions…

A:

Representing Maps

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 37: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

RFSs versus Vectors for SLAM

Vector Based Mapping and SLAM

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 38: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

RFSs versus Vectors for SLAM

RFS Based Mapping and SLAM

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 39: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

• System model:

• Vector-based formulations:

– Bayes filter

– Batch estimation

Data association

Motion input

Robot pose

Landmark position

Noise parameters

SLAM Formulations Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 40: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

• RFS-based formulations:

– Bayes filter

– Batch estimation

• Need to examine the relationship between the RFS and random-vector forms of the high-lighted distributions

Measurement likelihood

Normalizing factor

Measurement likelihood

Pose / map distribution

SLAM Formulations Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 41: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

• Janossy density for RFS

• Pick 1 / m! possible map ordering • Fix the map size to m, such that the cardinality distribution:

RFS density

Random-vector density

SLAM Pose/Map Distribution Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 42: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

• Set integral:

• Fix the map size: RFS integral

Random-vector integral

SLAM Normalizing Factor Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 43: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

• Multi-target likelihood RFS

measurement likelihood

SLAM Measurement Likelihood Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 44: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

• Select one data association hypothesis :

• Assume for all associated landmarks that

• In addition, for all non-associated measurements, assume

Random-vector measurement

likelihood

RFS measurement

likelihood

SLAM Formulations Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 45: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

• The RFS formulation is equivalent to the vector-based formulation when: – Map size is fixed / deterministic (with one ordering) – Data association is assumed – Probability of detection equals 1 for associated landmarks – Probability of non-associated measurements being clutter equals 1

• RFS-SLAM is a generalization of random-vector SLAM

SLAM Ideal Detection Conditions Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 46: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

From Point Process Theory: A Random Finite Set can be approximated by its first order moment – The Intensity function [Mahler 2003, Vo 2006]. kv

How to do RFS SLAM – PHD Approximation Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 47: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

From Point Process Theory: A Random Finite Set can be approximated by its first order moment – The Intensity function [Mahler 2003, Vo 2006]. kv

has the following properties:

kv

RFS SLAM – Intensity Function Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 48: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

From Point Process Theory: A Random Finite Set can be approximated by its first order moment – The Intensity function [Mahler 2003, Vo 2006]. kv

has the following properties: 1. Its integral, over the set, gives the estimated number of elements within the set.

kv

RFS SLAM – Intensity Function Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 49: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

From Point Process Theory: A Random Finite Set can be approximated by its first order moment – The Intensity function [Mahler 2003, Vo 2006]. kv

has the following properties: 1. Its integral, over the set, gives the estimated number of elements within the set. 2. The locations of its maxima correspond to the estimated values of the set members.

kv

RFS SLAM – Intensity Function Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 50: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

From Point Process Theory: A Random Finite Set can be approximated by its first order moment – The Intensity function [Mahler 2003, Vo 2006]. kv

has the following properties: 1. Its integral, over the set, gives the estimated number of elements within the set. 2. The locations of its maxima correspond to the estimated values of the set members.

kv

Intensity function can be propagated through the Probability Hypothesis Density (PHD) filter.

RFS SLAM – Intensity Function Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Page 51: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

E.g. 2 Features located at x=1 and x=4 with spatial variance: i.e. Feature set {1, 4} [Mahler 2007]. Suitable Gaussian Mixture PHD:

Example: 1D Intensity Function (PHD) 12 =σ

−−+

−−= 2

2

2

2

2)4(exp

2)1(exp

21)(PHD

σσσπxxx

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

Page 52: Robotic Navigation - Experience Gained with RADAR...Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM 2 nd Workshop on Alternative

E.g. 2 Features located at x=1 and x=4 with spatial variance: i.e. Feature set {1, 4} [Mahler 2007]. Suitable Gaussian Mixture PHD:

Example: 1D Intensity Function (PHD) 12 =σ

−−+

−−= 2

2

2

2

2)4(exp

2)1(exp

21)(PHD

σσσπxxx

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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E.g. 2 Features located at x=1 and x=4 with spatial variance: i.e. Feature set {1, 4} [Mahler 2007]. Suitable Gaussian Mixture PHD:

Example: 1D Intensity Function (PHD) 12 =σ

−−+

−−= 2

2

2

2

2)4(exp

2)1(exp

21)(PHD

σσσπxxx

Note: Maxima of PHD occur near x=1 and x=4 and

targets!of No. 2 1 1 )(PHD ==+=∫ dxx

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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E.g. 2 Features located at x=1 and x=4 with spatial variance: i.e. Feature set {1, 4} [Mahler 2007]. Suitable Gaussian Mixture PHD:

Example: 1D Intensity Function (PHD) 12 =σ

−−+

−−= 2

2

2

2

2)4(exp

2)1(exp

21)(PHD

σσσπxxx

Note: Maxima of PHD occur near x=1 and x=4 and

targets!of No. 2 1 1 )(PHD ==+=∫ dxx

Important Point: A PHD is NOT a PDF, since in general it does not integrate to unity!

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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Gaussian mixture representation of intensity function, showing peaks at feature locations at time k-1. Notice 2 features at (5, -8) represented by single, unresolved Gaussian with mass 2. Black crosses show true feature locations.

RFS SLAM – Intensity Function Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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Gaussian mixture representation of intensity function at time k. Peaks at feature locations (5, -8) now resolved - 2 Gaussians, mass 1. Note feature at (-5, -4) has reduced local mass, due to a small likelihood over all measurements.

RFS SLAM – Intensity Function Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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Comparative results for the proposed GM-PHD SLAM filter (black) and that of FastSLAM (red), compared to ground truth (green).

RFS Versus Vector Based SLAM Results based on single feature strategy:

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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The raw dataset at a clutter density of 0.03 . -2m

RFS Versus Vector Based SLAM Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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The estimated trajectories of the GM-PHD SLAM filter (black) and that of FastSLAM (red). Estimated feature locations (crosses) are

also shown with the true features (green circles).

RFS Versus Vector Based SLAM Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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Feature number estimates.

RFS Versus Vector Based SLAM Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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Sample data registered from radar.

RFS Versus Vector Based SLAM Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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Extracted point feature measurements registered to odometry.

SLAM input: Odometry path + radar data

RFS Versus Vector Based SLAM Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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EKF, FastSLAM and PHD-SLAM with Radar data.

RFS Versus Vector Based SLAM NN-EKF FastSLAM PHD-SLAM

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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Autonomous Kayak Surface Vehicle with Radar

Singapore – MIT Alliance: CENSAM Project • Environmental monitoring of coastal waters. • Navigation and map info. necessary above/below water surface. • Fusion of sea surface radar, sub-sea sonar data for combined surface/sub-sea mapping.

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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Singapore – MIT Alliance: CENSAM Project Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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• Surface and sub-sea data. • Verification of radar/sonar data with coastal satellite images.

Singapore – MIT Alliance: CENSAM Project Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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Coastal Mapping, Surveillance, HARTS / AIS verification Mobile platform can remove blind spots from land-based radar. Video: CoastalModelling.avi

Singapore – MIT Alliance: CENSAM Project

Video: CoastalandAIS.avi

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GPS Trajectory (Green Line), GPS point feature coordinates (Green Points), Point feature measurement history (Black dots).

RFS Versus Vector Based SLAM Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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Top: Posterior MHT SLAM estimate (red). Bottom: Posterior RB-PHD SLAM estimate (blue).

Ground truth (Green).

RFS Versus Vector Based SLAM Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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(Red) MHT SLAM Feature Number estimate. (Blue) PRB-PHD SLAM Feature Number estimate.

(Green) Actual Number to enter FoV at each time index.

RFS Versus Vector Based SLAM Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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RFS Versus Vector Based SLAM Results based on multi-feature strategy: Videos: Victoria park with added clutter (21,500 clutter detections) MH-FastSLAM with clutter RB-PHD-SLAM with clutter. Parque O’Higgins with “natural” clutter MH-FastSLAM with clutter (joggers in park) RB-PHD-SLAM with clutter.

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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• Objective: Show that Random Finite Set (RFS) SLAM is a generalization of the random-vector based SLAM formulation

• Approach: Mathematically show that the RFS Bayes Filter can be reduced to the Bayes filter in its random-vector form, under a set of ideal detection conditions. – This can also be shown for the batch estimation approach /

formulation

• Ideal Detection Conditions:

– Perfect detection of landmarks – No clutter / false alarms – Map size is deterministic – Assumed data association

hypothesis Close to ideal detection

conditions Non-ideal detection

conditions

Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

RFS Versus Vector Based SLAM

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• Importance of findings: – Understanding of why RFS-based SLAM algorithms perform better

under non-ideal detection conditions – Understanding of why vector-based SLAM algorithms perform better

under ideal detection conditions

• Experimental Validation: – Ideal vs. non-ideal detection conditions – RB-PHD-SLAM vs. FastSLAM – Simulations and real datasets

RFS Versus Vector Based SLAM Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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FastSLAM RB-PHD-SLAM FastSLAM RB-PHD-SLAM Close to ideal conditions Non-ideal conditions

RFS Versus Vector Based SLAM Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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1. Mapping, Tracking & SLAM at U. Chile. 2. Sensor Measurement & Detections:

• Landmark Existence and Spatial Uncertainty.

3. Simultaneous Localisation & Map Building (SLAM).

• A Random Finite Set (RFS) Approach.

• Set Based Likelihood Formulation.

• RFS SLAM – A generalization of Random Vector SLAM.

• RFS versus Vector Based SLAM – Results.

4. Future Work in RFS based Mapping/SLAM.

Presentation Outline Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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1. Improve robustness of autonomous sensing systems. 2. Probabilistic sensor modelling - improve estimates of

detection statistics, e.g. - based on occlusions. 3. Other FISST SLAM techniques based on Labelled Multi-Bernoulli

(MeMBer) Filter. 4. Metrics beyond OSPA for the intuitive evaluation and

comparison of SLAM maps.

Conclusions & Future Work Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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The Research Teams:

Javier Ruiz-del-Solar, Daniel Lühr, Felipe Inostroza, Keith Leung, John Mullane, Ba-Ngu Vo, Samuel Keller, Chhay Sok, Liu Bingbing, Ebi Jose, Tang Fan, Lochana Perera, Zhang Sen, Zen Koh, Bimas Winaju, Sardha

Wijesoma, Andy Shacklock, Akshay Rao, Tan Chai Soon ………..!

Acknowledgements Mapping, Tracking & SLAM at U. Chile Sensor Measurements & Detections SLAM Future Work in RFS Based SLAM

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• Open source, with BSD-3 License • Dependencies:

– Boost::math_c99 1.48 – Boost::timer 1.48 – Boost::system 1.48 – Boost::thread 1.48 – Eigen3

• Tested on Ubuntu 13.04 • Template library

– Define your own process models – Define your own measurement models

• Includes an implementation of the RB-PHD Filter • Includes a 2-d SLAM example • Well documented • Will be updated with new published research • Download at: https://github.com/kykleung/RFS-SLAM

C++ Library for RFS SLAM

2nd Workshop on Alternative Sensing for Robot Perception – IROS 2015