Simultaneous Localization and Mapping (SLAM)

8/4/2019 Simultaneous Localization and Mapping (SLAM)

http://slidepdf.com/reader/full/simultaneous-localization-and-mapping-slam 1/66

Minya UniversityFaculty of Engineering

CSE 2011

Simultaneous Localization and Mapping

Ibraam Mahroos Fekri Mostafa Hassan Khamees

Omar Shaaban Ibrahim

Dr. Hassan Ali Hassan

Contents

• Introduction

• Where we are ?

• SLAM Problem

• Objectives

• Linear Dynamic Systems

• Extended Kalman Filter

• EKF SLAM Algorithm

• Data Association• Computer Vision

• Work

Introduction

14/04/2012SLAM4

Localization Determine position given a priori map of the environment.

Mapping

Generating map of the environment when the pose is accuratelyknown from auxiliary source.

Where we are?

14/04/2012SLAM5

Localization

Mapping

Planning

SLAM Problem

14/04/2012SLAM6

Simultaneous Localization And Mapping (SLAM)

An autonomous vehicle exploring an unknown environmentwith onboard sensor and incrementally build a map of thisenvironment while simultaneously using this map to

computing the vehicle location.

SLAM combines the two problems of localization andmapping.

Objective

14/04/2012SLAM7

Solving the SLAM problem in a static environmentusing vision sensor and Extended Kalman Filter as

an estimator.

Given Robot’s controls.

Onboard sensor (stereo camera).

Estimate

Map representation of the environment (feature basedmap).

Trajectory of the robot.

Linear dynamic systems

14/04/2012SLAM8

Dynamic model:

: State vector of the system at instant t = k T (assumed to be complete, Markov assumption).

: known control inputs.

: unknown system noise (can be due to systemperturbations, model imperfections…etc.).

Sensor model

14/04/2012SLAM9

Measurement model

measurements vector at instance time

: unknown system noise (imprecision in theobservation process and/or model)

Optimal Estimation

14/04/2012SLAM10

Available information up to time k:

= * , , 3 , … U− = * u, u , 3 , … −

Assuming independent white Gaussian noises for thestate and the measurement, this system can besummarized in these conditional PDFs:

|− ≜ −, − Prediction ≜(| , −) Estimation

Optimal estimation(minimum mean squared error)

Bayesian recursive estimation

14/04/2012SLAM11

|− = − , −) − − Prediction

= ) |− Update

No analytical solution in general

Linear Gaussian case (additive white Gaussian

noise, AWGN)

Kalman Filter

14/04/2012SLAM12

= − + − + − = + Prediction

− = − + −

− = −

Update

= −(− )−

= − ( −) = ( )−

MMSE Estimator

Extended Kalman Filter

14/04/2012SLAM13

Nonlinear dynamic systems

+= ( , ) = ( )

Linear approximation of dynamic model andmeasurement model

Prediction − = (− , − ) − = − −

Update = −(− )−

= − ( (−)) = ( )−

Best linear Estimator

EKF-SLAM Algorithm

14/04/2012SLAM14

Consider a mobile robot moving through anenvironment taking relative observations of a

number of unknown landmarks using a sensorlocated on the robot.

14/04/2012SLAM15

EKF-SLAM Algorithm cont.

14/04/2012SLAM16

Data Association

14/04/2012SLAM17

Data association consists in relating sensorobservations with the elements included in the map.

Interpretation tree.

Data Association cont.

14/04/2012SLAM18

Individual Compatibility Nearest Neighbor Algorithm (ICNN)

Why data association is so difficult?

14/04/2012SLAM19

14/04/2012SLAM20

14/04/2012SLAM21

Why Vision ?

»Human use vision

» Exclusive details

»Cheaper

14/04/2012SLAM22

Perception

» Ambient environment!

» Acquire knowledge

»Communicate» Interact

14/04/2012SLAM23

Sensors

» Why … To Sense ?

» Proprioceptive / Exteroceptive

» Proprioceptive: Motor speed – Wheel Load

» Exteroceptive: Light intensity - Distance

» Active / Passive

» Active: Wheel quadrature encoder

» Passive: Microphones – CMOS camera

14/04/2012SLAM24

Vision Sensors Tech.

Charged Coupled Device

»CMOSComplementary Metal Oxide Semi

Conductor

14/04/2012SLAM25

Mono Vs. Stereo

»Details

» 2D or 3D

»Range of Vision» Estimated Vs. Virtual 3D

14/04/2012SLAM26

Camera Calibration

»Why be done?

» Intrinsic parameters

» Focal length» Principal point

» Extrinsic parameters

» denote the coordinate system transformation from3D world coordinate to camera 3D coordinates

14/04/2012SLAM27

Depth Estimation

»Depth needed for interactions

»Depth for stereo using Triangulation

14/04/2012SLAM28

Depth Estimation

14/04/2012SLAM29

Depth Estimation

14/04/2012SLAM30

Vision Algorithms

14/04/2012SLAM31

»Detection

»Description

Harris Algorithm

14/04/2012SLAM32

» Feature Detector

» Affine-Invariant

» Corner Measure» Gaussian Scale-Space

Corner Measure

14/04/2012SLAM33

, = , , =1

2 ( − +

Gaussian Scale-Space

14/04/2012SLAM34

, = ⨂ . 6.7

Harris Algorithm

14/04/2012SLAM35

»Combination between Corner Measure

and Scale-space

» Scale-Invariant detector

SIFT Algorithm

14/04/2012SLAM36

»S cale I nvariant F eature T ransform

»Detector and descriptor

Objects

14/04/2012SLAM37

14/04/2012SLAM38

Result

14/04/2012SLAM39

Algorithm

14/04/2012SLAM40

»Constructing Scale-Space

» LOG Approximation

» Finding Keypoints» Accurate Keypoints

» Assigning Keypoints Orientation

»Generating SIFT Features

Scale-Space

14/04/2012SLAM41

Laplacian of Gaussian

14/04/2012SLAM42

, , = , , ∗ ,

Laplacian of Gaussian

14/04/2012SLAM43

Finding Keypoints

14/04/2012SLAM44

Finding Keypoints

14/04/2012SLAM45

Finding Keypoints

14/04/2012SLAM46

Accurate Keypoints

14/04/2012SLAM47

Descripting

14/04/2012SLAM48

» Assign orientations for Keypoints

»Generate SIFT features

Hardware

14/04/2012SLAM49

» Stereo camera

»Mono camera

» IP camera» 2 Mono = Stereo

Results

14/04/2012SLAM50

Mobile robot

Is two wheel differential driverobot(2WD) controlled byarduino and motor shieldfrom PC, laptop and mobileover Wi-Fi network usingWiFly shield.

The robot has two camera towork as stereo camera andtwo quadrature wheel

encoders to measure thedistance each wheel hastraveled.

14/04/2012SLAM51

Mobile robot

Advantages of differential drive mobile robot:

One of the simplest mobile robot constructions is achassis with two fixed wheels.

It can rotate freely in all directions Simple kinematic model

14/04/2012SLAM52

The Robot Kinematic Model

V L is the left wheel’s linear

velocity.

V R is the right wheel’slinear velocity.

V S is the robot linear velocity

D is the wheel base Ө is the robot angle

14/04/2012SLAM53

Robot Equation of Motion

( )cos()

( )sin() ( )

Where X, Y are the position of the robot with respect

to global frame and Ө is the angle between axis Xand the robot axis.

14/04/2012SLAM54

Robot Controller

Using Arduino UNO

It's an open-source physical computing platform based

on a simple microcontroller board, and a developmentenvironment for writing software for the board.

14/04/2012SLAM55

Arduino UNO

Is a microcontroller boardbased on the ATmega328and it has:

14 digital I/O pins (of

which 6 can be used asPWM outputs)

6 analog inputs

16 MHz crystal oscillator

USB connection

Power jack, ICSP header,and a reset button.

14/04/2012SLAM56

Arduino UNO

Advantages:

• Provide a simple interface.

• Easy to program

• USB over current protection

14/04/2012SLAM57

Direction and Speed Controller

» Is Arduino Motor shielduses L298P h-bridgechip which allow to drivetwo 7-12V DC motors

with maximum 2Acurrent.

» This shield can bedirectly mount ontostandard Arduino UNO,Duemilanove andArduino Mega.

14/04/2012SLAM58

» Advantages:

The motor shield lets youcontrol a whole bunch ofcurrent (good for motors)with an itty-bitty signal(good for Arduinos).

14/04/2012SLAM59

Communication

» Using WiFly Shield» Arduino able to connect

to 802.11b/g wirelessnetworks.

» The featuredcomponents of the shieldare a Roving Network'sRN-131G wireless

module and anSC16IS750 SPI-to-UARTchip.

14/04/2012SLAM60

Controlling the Robot

Programming the Controller:

» Write program making Arduino work as a web serverstoring an HTML page.

» Upload the program to Arduino .

14/04/2012SLAM61

Controlling the Robot

» Plug in Arduino ,Wifly and Motor Shield power.

» Waitting WiFly shield to join the Wi-Fi network

» Open Internet browser

» Connect to Arduino server» Browser send a request to Arduino server

» HTML page for contol Buttons loaded

14/04/2012SLAM62

Future Work

» Autonomous

» Sensor Fusion

» Embedded Processing

» Better Estimator (for large scale environment)

14/04/2012SLAM64

Problems

Simultaneous Localization and Mapping (SLAM)

Documents

Dynamic Visual Simultaneous Localization and Mapping for ... · Takeishi and Yairi, Dynamic Visual SLAM for Asteroids, i-SAIRAS 2016 20 Summary: Asteroid SLAM ~20km ~50m. ground sta

SLAM: Simultaneous Localization and Mapping

Marginal Particle and Multirobot Slam: SLAM=‘SIMULTANEOUS LOCALIZATION AND MAPPING’ By Marc Sobel (Includes references to Brian Clipp Comp 790-072 Robotics)

A Tightly-coupled Semantic SLAM System with Visual ... · The semantic SLAM (simultaneous localization and mapping) sys-tem is an indispensable module for autonomous indoor parking

ClusterSLAM: A SLAM Backend for Simultaneous Rigid Body Clustering …orca.cf.ac.uk/125005/1/ClusterSLAM_ICCV2019.pdf · (AuT), which is achieved via Simultaneous Localization and

Simultaneous Localization & Mapping - SLAM Praveen K Santhanam – pks6

An Analysis of Simultaneous Localization and Mapping (SLAM

SLAM: Robotic Simultaneous Location and Mappingspacecraft.ssl.umd.edu/academics/788XF14/788XF14L17/788XF14L17... · SLAM: Robotic Simultaneous Location and Mapping William Regli Department

Offline Simultaneous Localization and Mapping (SLAM) … · – Odometer data – Range measurements • Scan matching impossible – “Random” measurements in corners – Data

A Solution to the Simultaneous Localization and Map Building (SLAM) Problem

Provisional Thesis Title: Simultaneous Localization and Mapping … · Simultaneous Localization and Mapping (SLAM) is the problem of building a map of an unknown environment by a

SLAM: Simultaneous Localization and Mapping · 2020. 1. 31. · 1 SLAM: Simultaneous Localization and Mapping Instructor: Chris Mavrogiannis TAs: Kay Ke, Gilwoo Lee, Matt Schmittle

Performance Analysis of the Microsoft Kinect Sensor for 2D Simultaneous Localization and Mapping (SLAM) Techniques

Simultaneous localization and mapping (SLAM) with an ......Simultaneous localization and mapping (SLAM) is a problem in which an autonomous robot is required to initiate in an unknown

slam robotic navigatin genetic localization

An Introduction to Robot SLAM (Simultaneous Localization ...example of SLAM (Simultaneous Localization and Mapping). Determining the location of objects in the environment is an instance

An Introduction to Robot SLAM (Simultaneous Localization ...ceit.aut.ac.ir/~shiry/lecture/robotics/Robot Navigation... · example of SLAM (Simultaneous Localization and Mapping)

Decoupled Localization and Sensing ... - vision.cornell.edu · with vision-based Simultaneous Localization and Mapping (SLAM) (e.g., [10,11]) or ICP-based tracking with RGBD input

Simultaneous Localization, Mapping and Moving Object … · Simultaneous Localization, Mapping and Moving ... Localization, mapping and moving object tracking are ... SLAM with detection

Introduction to Mobile Robotics Welcomeais.informatik.uni-freiburg.de/teaching/ss20/robotics/slides/00-intro.pdf · SLAM: Simultaneous Localization and Mapping 13. SLAM: Landmark-based