1. Advisor: Dr. Arun Lakhotia
Terrain Mapping and Obstacle Detection for Unmanned Autonomous Ground Vehicle Without Sensor Stabilization
October 20, 2006
Amit Puntambekar
Advisor: Dr. Arun Lakhotia
Team CajunBot

2. Presentation Overview
Introduction and motivation
Related work
Terrain mapping and obstacle detection algorithm
Sensor error handling
Algorithm evaluation
Conclusion and future work
Estimated presentation time: 50 minutes

3. Top: Mars Rover by NASA, Bottom: iGator by iRobot
DARPA Grand Challenge
History
Autonomous Ground Robots
Application of AGV’s
Examples
Top: Mars Rover by NASA, Bottom: iGator by iRobot

4. DARPA Grand Challenge
Grand Challenge 2004
Grand Challenge 2005

5. Components of Autonomous System
Hardware – Sensors, Electronics, etc.
Software
Obstacle Detection
Path Planning
Steering

6. Obstacles
Man Made
Natural

7. Types of Obstacles Static - Rocks, Cones, Steep Slopes, etc.
Dynamic – Moving Cars, Gate, etc.
Negative Obstacles – Ditches, Potholes, etc.

8. Motivation Timely Obstacle Detection
-Top speed of vehicle: 25 mi/hr (11.17 m/s).
-Even a second delay in detecting obstacle might be fatal.
Static and Dynamic Obstacles
Negative Obstacles

9. Sensors
GPS
Position information
INS
Orientation
LIDAR
Range

10. GPS/INS Principle of Operation Data Format Erroneous Conditions
Position
5 Hz
Position +
Orientation
GPS
INS
100 Hz

11. LIDAR
0 degree
180 degree

12. LIDAR Terminologies LIDAR Beams Scan Laser Beams Time Stamp Range
1 Scan = 180 beams
75 Scans per Sec

13. LIDAR Mounting Parallel to Ground Used by CMU Minimum obstacle size
Slopes as obstacles
Sensitive to
Vibrations
Mountings
Air Pressure

14. LIDAR Mounting Sweeping the terrain Scans sweep terrain
Successive scans are geographically close
Consecutive scans on flat ground

15. LIDAR Mounting Vertical Mounting Combination Mounting Team GRAY
Data Discontinuity
Combination Mounting

16. Algorithms for Sweeping LIDARs
Consecutive scan analysis
The laser scans incrementally sweep the surface
Analyzing the consecutive scans to determine change in geometry of the terrain
Data Discontinuity
Detect Discontinuity in data
Team GRAY, METIOR
Plane Fitting
Best fitting plane computation
Virginia Tech
Slope Computation
Change in slope of the scans is computed
CMU, Team ENSCO

17. Prior Work Review Dependent on Incremental scan sweeping
Flat terrain
Sensor mountings
Will not work if sensor mounting is changed

18. Off-Road Conditions- Bumps
Indoor Vs. Off-road Environments
Effect of Bumps 2 3 4 1
Scattered scans due to bumps

19. Top: Sandstorm from CMU; Bottom: IRV from Indiana Robotics
Sensor Stabilization
Specific Sensor Stabilizers
Vehicle Suspensions
22 out of the Grand Challenge finalist team had vehicle suspensions or hardware sensor stabilizers to mitigate bumps.
Teams like CMU, IRV had both
CajunBot was the only entry without sensor stabilizer and suspensions
Top: Sandstorm from CMU; Bottom: IRV from Indiana Robotics

20. Sensor Stabilizers Cost
- The cost of the CMU Gimbal is approximately $70,000.
Single Point of Failure

21. Research Contribution
Off-Road Obstacle Detection System
Without sensor stabilization
Not sensitive to sensor mountings
Accounts for GPS errors
Scales well with number of sensors

22. Core Algorithm Obstacle Detection Algorithm Theory
Implementation for a real time system – CajunBot
Error Handling

23. Algorithm Theory
Points
Slope Computation
Triangle Formation

24. Algorithm Theory (Continued)
High Absolute Slope
High Relative Slope
Height Discontinuity
Obstacle
Triangle Analysis

25. Obstacle Detection High Absolute Slope
- Large surfaces where triangles can be formed
Eg: Wall, cars, etc.
High Relative Slope
- Obstacles on slope
- When obstacles are not large enough to register three LIDAR beams to form triangles
- Negative obstacles
High Elevation Change
- Narrow obstacles like poles
- Negative Obstacles
angle
Top: Virtual Triangle on a wall like obstacle
Bottom: Obstacle on a slope

26. . . . . . . . . . . . . Real Time System Slope Computation Pos +
Position
Pos +
Orientation
Data Fusion +
(Range, Angle),75

27. Effect of Bumps Scans Scattered due to bumps
Consecutive scans might be geographically far apart 2 3 4 1

28. Spatial Griding .. . … Slope computation . . . . . . Data Fusion +
Position
Position, Location
. . .
. . . .. . …
Data Fusion +
Slope Grid
(Range, Angle),75

29. Data Consistency
Temporal accuracy of GPS
GPS Drift 1 2 3 4

30. Sensor Error - GPS Drift
X Axis: Time (s)
Y Axis: Height (m)
What is GPS Drift ?
Gradual drift in the GPS data
Effects of GPS Drift?
Only temporally close data can be compared
Factors causing GPS Drift
Hardware and connectivity with satellites
Moving
0.13
0.20
Stationary
Graph: GPS Z Vs. Time
Data Collected on a flat parking lot. Vehicle traveling at 3m/s

31. Handling GPS Drift
Temporal Data Ordering
GPS stable for 3-4 seconds

32. Obstacle Detection Obstacle Cell Analysis Absolute Slope
Relative Slope
Height Discontinuity
Terrain Obstacle Map (TOM) Grid

33. Obstacle Detection – TOM Grid Analysis
(Max Orientation,
Min Orientation,
Max Height,
Min Height,
Num of Centroids,
Num of Hits)
Potential Obstacle Determination
High Absolute Slope
Absolute Orientation > Threshold
High Relative Slope
Difference in Orientation > Threshold &
Difference in Height > Threshold
High Elevation Change … .. .
…..
Terrain Obstacle Map
Confidence Factors

34. Terrain Obstacle Map (TOM)
Dynamic Obstacles
New Data, T_new
Last Access Time Stamp,
T_old
Dynamic Obstacles
registered as obstacle at every location
Refresh Grid
Grid Refreshing
- TOM in a spatio-temporal grid
- Refresh TOM Cells if existing data and new data are not temporally close
- Aging based on access time stamp … .. .
…..
Terrain Obstacle Map (TOM)

35. Sensor Error GPS Spike What is GPS Spike ?
-Sudden change in the GPS data in a very short time interval.
-The elevation data is more prone to GPS Spikes
Causes for GPS Spike
-Weak Signal
-After ‘Dead Reckoning’
Graph: GPS (Z) Vs. Time
30m
X-axis : Time(s)
Y-axis: Height(m)
NQE 2005 Data

36. GPS Spike Data Playback
Effect of GPS Spike
GPS Spike Data Playback
Graph: GPS Z Vs. Time

37. GPS Spike: Reason for False Obstacles
Corrupted data enters system
Slope computation gets erroneous
Data Filter

38. Detecting GPS Spike Median filter monitors INS Data
Erroneous data is discarded

39. Core Algorithm- Revised
Terrain Modeling
Obstacle Detection

40. Effect of Bumps - II INS, LIDAR data fusion -Mounting INS on LIDAR
-Good Suspensions
-Sensor Stabilizers
-Rigid Platform
LIDARS
GPS
INS
Rigid Platform

41. Effects of Bumps INS, LIDAR Data Rate Mismatch LIDAR X Angle INS TimeX t2

42. Sensor Fusion CBWare - Data interpolation support
Robots with sensor stabilizers can fuse the most recent data from sensors
In CajunBot data is interpolated based on time of production t1 t2
Time
Angle
LIDAR
INS X

43. Algorithm Evaluation Ability to utilize bumps to see further
Accuracy of results
Algorithm complexity
Scalability
Different obstacle types
Sensor orientation independence

44. Data Sets Logged data from 2005 GC
Testing in a controlled environment with CajunBot-II
Testing in a simulated environment - CBSim

45. Evaluation – Effects of Bumps
Obstacle detection distance increases linearly with severity of bumps experienced

46. Testing in a controlled environment with CajunBot-II Effect of bumps
Experimental Setup
Obstacle detection without Bumps
With Bumps
Without Bumps
Distance
42.6 m
28.5 m
False Obstacles
NIL
Comparison table
Obstacle detection with bumps

47. Scalability Sensor Specific Computation Data Specific Computation
Results based on analyzing CajunBot-II logged data on a Dell machine with 3.2 GHz Intel Processor and 1 GB RAM with full load (all other CajunBot software modules running) on Fedora Core 2 operating system

48. Scalability and Bumps
Results based on analyzing the 2005 GC final run logged data on a Dell machine with 1.6 GHz Intel Processor and 1 GB RAM with full load (all other CajunBot software modules running) on Fedora Core 2 operating system

49. Sensor Orientation Independence
Run 1
Top sensor
Orientation (r, p, h)=(0, -1.5, 1)
Offsets (X, Y, Z)=(0.25, 1, 0.25)
Bottom Sensor
Orientation (r, p, h)=(0, -3, 1.5)
Offsets (X, Y, Z)=(0.25, 1.5, -0.5)
Run 2
Top sensor
Orientation (r, p, h)=(2, -4.5, 0)
Offsets (X, Y, Z)=(0, 1, 0.5)
Bottom Sensor
Orientation (r, p, h)=(-2, -4, 2)
Offsets (X, Y, Z)=(0, 2, 0)
CajunBot with two distinct sensor orientations

50. Comparison with different obstacle shapes at varying speed
HD: Height Discontinuity, AS: Absolute Slope, RS: Relative Slope
Considerable increase in speed, negligible decrease in efficiency
4%, 3.2%, 3.9% decrease in detection distance among the three shapes when the speed increases by 150 %

51. Limitations of the algorithm
High precision INS required
Sensitive to Boresight Misalignment
Angle at which LIDAR is mounted w.r.t INS
Fusion data from multiple LIDARs
Limited LIDAR reflectivity
Water
Black surfaces – tar roads, etc.

52. Conclusion An Obstacle Detection system Efficient Spatio-Temporal Grid
Without sensor stabilization
Takes advantage of bumps to see further
Not sensitive to sensor mountings
Accounts for GPS errors
Scales well with number of sensors
Handles dynamic obstacles
Efficient Spatio-Temporal Grid
Evaluation of system
On logged data, live on CajunBot-II, and simulator

53. Future Work Dynamic obstacle detection Obstacle Classification
Detecting trajectory and speed
Obstacle Classification
Vegetation, mesh, etc

54. Questions ?

55. Thank you

56. Backup Slides

57. Boresight Angles / OffsetsHs
GPS x p n y
INS
Body on which the sensor is mounted
Laser sensor
Laser beam
Actual Ground Line
Computed Ground Line P R

58. Limitations of the algorithm
High precision INS required
Sensitive to Boresight Misalignment
Angle at which LIDAR is mounted w.r.t INS
Fusion data from multiple LIDARs
Water, black surfaces do not reflect LIDAR

59. Effect of Mounting Angles on Sensor Data
LIDAR Errors
Boresight Misalignment L1 L2 L3 L4 L5 L7 L6
Effect of Mounting Angles on Sensor Data

60. Sensors Used
LIDARs
GPS
INS

61. GPS/INS Principle of Operation Data Format Erroneous Conditions
Position
5 Hz
Position +
Orientation
GPS
INS
100 Hz

62. LIDARS Principle of Operation – Time of Flight
Data Format – 75 Hz at 0.25 degree offset
Erroneous Conditions
Laser Beams

63. Data Handling
To keep multiple copies of 2 minute worth of data in memory would require 550 MB of RAM
Updating multiple copies is expensive

64. Data Handling

65. . . . . . . LIDAR Terminologies LIDAR Beams Laser Scan
1 scan = 180 beams
75 scans per sec