1 / 65

Amit Puntambekar Advisor: Dr. Arun Lakhotia Team CajunBot cajunbot

Terrain Mapping and Obstacle Detection for Unmanned Autonomous Ground Vehicle Without Sensor Stabilization. October 20, 2006. Amit Puntambekar Advisor: Dr. Arun Lakhotia Team CajunBot www.cajunbot.com. Presentation Overview. Introduction and motivation Related work

angie
Download Presentation

Amit Puntambekar Advisor: Dr. Arun Lakhotia Team CajunBot cajunbot

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

  11. LIDAR 0 degree 180 degree

  12. LIDAR Terminologies Laser Beams Time Stamp • LIDAR • Beams Range Scan • 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 • 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. Sensor Stabilization • Specific Sensor Stabilizers • Vehicle Suspensions • 22 out of the 23 2005 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 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 Position Position, Location . . . . . . . . . . . . Data Fusion + Slope Grid (Range, Angle),75

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

  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 Difference in Height > Threshold Terrain Obstacle Map Confidence Factors

  34. 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 ErrorGPS Spike Graph: GPS (Z) Vs. Time • 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’ 30m X-axis : Time(s) Y-axis: Height(m) NQE 2005 Data

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

  41. LIDAR Angle INS Time t1 t3 Effects of Bumps • INS, LIDAR Data Rate Mismatch 0 X 0 t2

  42. LIDAR Angle INS Time t1 t2 Sensor Fusion Interpolation • 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 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 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 %

More Related