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Sensor Fusion on TerraMax
Dr. Zhiyu XiangAndy ChienProf. Umit Ozguner
Feb. 17th, 2004, Tuesday
Lecture on EE753.02
System Overview
CAMERAS
MONO-VISION computer
Linux
STEREO-VISION computerLinux
Sensor and Sensor Fusion computerLinux
LADARs
Radars
Short distance sensors
Map and high level path planning computer
High level Control
Low level Sensing
Low Level ControlQNXGPS
Alarm monitoring and heartbeat
Compass
INS
Internal sensors
External switches
E-Stop
Brake actuators
Throttle Control
Steering motor
Shifting
CAMERAS
High Level Sensor System Overview
Laser Radar(LADAR)
Laser Radar(LADAR)
RadarRadar Mono Vision
Mono Vision
Stereo Vision
Stereo Vision
SonarSonar
DGPSDGPS
INSINS
COMPASS
COMPASS
PositionFusion
PositionFusion Sensor FusionSensor Fusion
Why Sensor Fusion?
Sensors have different perceptive ability against environment;Sensors have different field of view;Even with the same type of sensors, we can: Enlarge the entire field of view by using
more sensors; Accumulate the information acquired at
different time to achieve better perception.
The Information available from GPS
1. Position in the Geodetic Coordinates (latitude, longitude, Altitude)2. Rate information. (Horizontal Speed, orientation to the true north.)3. The GPS Precise Time.
GPS - AdvantagesAdvantages: Satellite-based radio navigation system; Provide information to the users of GPS
receivers worldwide in all weather conditions, free of charge;
Reduces overall system costs by eliminating the need for a separate base station to obtain decimeter-level accuracy
Protects against shock, water, and dust, extending the life of the receiver
Virtually eliminates the effects of multipath using NovAtel’s patented Pulse Aperture Correlator™ (PAC) tracking technology
GPS - Features
- Accepts OmniSTAR L-band differential corrections (subscription required)
Shock, water, and dust resistant Three RS-232 serial ports capable of
rates up to 230,400 bps Power and communication status LED
indicators Field-upgradeable firmware
INS System
Dynamic Roll, Pitch Body to Earth Frame Angles 3-Axis Vehicle Body Rates 3-Axis Vehicle Body or Earth Accelerations
INS - Features
Fiber Optic Gyro Stability < 20°/hr Fully Compensated Angular Rate and Linear Acceleration Outputs SAE (Earth Coordinate) Navigation Frame Automotive Compatible 10-30 VDC Input Supply Analog & Digital Outputs
One Example Application of INS
Inertial systems are frequently used in actively stabilized platforms. Actively stabilized means that a series of motors and gimbals in conjunction with a inertial sensor work actively to hold the platform stationary.
•Active stabilization systems are typically used to point cameras or antennae on a moving plane, helicopter, ship, train, or even RV. There are also cases where cameras permanently attached to the ground are stabilized for wind and vibrational forces.
Compass
The HMR3000 Digital Compass Module is a three-axis compass featuring 0.5 degree accuracy and a fluidic tilt sensor for +/- 45 degree compensation and digital serial bus interface (RS-485 or RS-232 options).
Why Fuse GPS/INS/Compass?
Accuracy. INS can lead to the unbounded growth of its error, even with the smallest amount of error in its measurements. This gives the rise to the need for an augmentation of the measurements by external aiding sources to periodically correct the errors. GPS can do that, with its bounded measurement error.
Why Fuse GPS/INS/Compass? (II)
Data Output Rate. The data output rate of GPS is 10 Hz at the most, which is insufficient for the positioning of a vehicle under autonomous control. On the contrary, the output of INS is much higher, even more than 100Hz on the digital signal output and no frequency limit on analog signal output. The integration of both can therefore satisfy the data output rate requirement.
Why Fuse GPS/INS/Compass? (III)
Data Availability. GPS is a line of sight, radio navigation system, and therefore GPS measurements are subject to signal outages, interference, and jamming, whereas INS is a self-contained, non-jammable system that is completely independent of the surrounding environment, and hence virtually immune to external disturbances. Therefore, INS can continuously provide navigation information when GPS experiences short-term loss of its signals.
Why Fuse GPS/INS/Compass? (IIII)
Compass can provide yaw, pitch and roll information continuously independent of other sensors. Although the data output rate is less than 20Hz, it can correct the yaw information integrated from INS yaw rate periodically.
Fusion Algorithm of GPS/INS/Compass
DGPSAntenna
DGPS Receiver
RS-232 Hardware
Interface
X Accelerometer
X Accelerometer
Z Accelerometer
Yaw Rate
Pitch Rate
roll Rate
INS SYSTEM
Yaw
Pitch
Roll
COMPASS
Position: X,Y,Z
Speed:
Yaw
GPS Output
Extend Kalman Filter Algorithm
Vehicle Status:Position: X,Y,Z; Speed: ; Accelerator: ;Yaw, Pitch, Roll; Rate of Yaw, Pitch and roll.
PC
Performance of LADAR
1.Angular Resolution: 1° / 0,5° / 0,25°
2. Response Time (ms): 13 / 26 / 53
3. Resolution (mm) : 10
4. Systematic Error (mm mode): 35
5. Statistical Error (1 Sigma): 10mm
6. Max. Distance (m): 80
7. Transfer rate: 9.6/19.2/38.4/500 kBaud
Obstacle Detection by LADAR
LADAR can tell the distance between the obstacle and the center of the LADAR
Some Scenarios for Vertical Scanning Laser (I)
LP
h
W
Scanning vertically
The minimum width and depth of the ditch can be decided by the wheel radius and the speed of the vehicle. The higher the Ladar is installed, the farther the ditch could be detected.
Radar System
Provides Information of objects in the lane up to 350 feet ahead. Advanced forward looking Doppler radar (24.725 GHz), providing distance, relative speed. Operate effectively night or day, in rain, fog, dust, or snow.
Ultrasonic Sensors
For short range obstacle detection.(Less than 5 meters.)Accuracy affected by temperature, moisture, etc.
What kind of confliction may happen between sensors?
IN Data Layer: same objects in the environment, their
position declared from sensors may be different (I.e., one sensor tells the range of 30 meter while the other tells 28 meter);
In Decision Layer: The decision of the observation may
conflict with each other.(I.e, No Obstacle VS. Obstacle).
How to solve the confliction between sensors? (I)
How does the confliction happen? Different perceptive character of sensors;
Range, accuracy, field of view, imaging sensor VS. range sensor, etc..
Changing of the surrounding environment;
False data input; Thresholds on processing algorithms; Different algorithms used.
How to solve the confliction between sensors? (II)
Measures to deal with the conflictions: For data layer:
Using Target Tracking techniques (Extend Kalman Filter);
For decision layer: Assign a confidence to each decision made by
the preprocessing of each sensor; Deduce the final decision with a deliberately
designed Deducing Table (Evidence Theory based deducing).
Map for High Level Sensor Fusion
East
North
50m
-50m
50m
High confidence of occupy
Low confidence of occupy
High confidence of empty
Unknown area
Vision Map
Laser Map
Type of the cells:
ROD
COV
POB
NOB
MOB
UKN
Algorithms for Fusion Map Updating
Map initialization
Get Mono-Vision Information at
Fusion map movement according to the GPS displacement between and
Discard cells outside the map and give initial values to newly shift-in cells
Broadcast confidence value to neighboring cells according to the model of position errors. (Gaussian noises)
Get new Observations from Stereo Vision, LADAR, Radar and Sonar modules.
Transforming the coordinates of different sensor modules to Sensor Map coordinates by using the calibration parameters.
Fusing the Sensor Map into the Fusion map by using the Dempster-Shafer Evidence theory.
Multi-sensor calibration
1kk
Deducing Table(I)
sem _ sem _ som _som _som _ sum _
fem _
fem _
fom _
fom _
fom _
fum _
Information from Sensor Map
Information
from
Fusion
Map
ROD COV POB NOB MOB UKN
ROD ROD(1)
COV if (a)ROD if (b)(1)
POB if (g), results (3);ROD if (h), results (5).
NOB if (g), results (3);ROD if (h), results (5).
MOB(3)
ROD(2)
COV ROD if (a)COV if (b)(1)
COV(1)
POB if (g), results (3);ROD if (h), results (5).
NOB if (g), results (3);COV if (h), results (5).
MOB(3)
COV(2)
POB ROD if (e), results (3);POB if (f),Results (4).
COV if (e), results (3);POB if (f), results (4).
POB(1)
NOB if (c)POB if (d)(1)
MOB(3)
POB(2)
NOB ROD if (e), results (3);NOB if (f), results (4)
COV if (e),Results (3);NOB if (f),Results (4).
POB if (c)NOB if (d)(1)
NOB(1)
MOB(3)
NOB(2)
MOB No prediction exists in the fusion map, replaced by UKN.
UKN ROD(3)
COV(3)
POB(3)
NOB(3)
MOB(3)
UKN(3)
Summary
By sensor fusion, the complementary information from different sensors are fully and best combined;Information acquired at different time is accumulated; Sensors are integrated together and a best decision was made upon that.