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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
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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
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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
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DARPA Grand Challenge Grand Challenge 2004 Grand Challenge 2005
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Components of Autonomous System
Hardware – Sensors, Electronics, etc. Software Obstacle Detection Path Planning Steering
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Obstacles Man Made Natural
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Types of Obstacles Static - Rocks, Cones, Steep Slopes, etc.
Dynamic – Moving Cars, Gate, etc. Negative Obstacles – Ditches, Potholes, etc.
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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
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Sensors GPS Position information INS Orientation LIDAR Range
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GPS/INS Principle of Operation Data Format Erroneous Conditions
Position 5 Hz Position + Orientation GPS INS 100 Hz
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LIDAR 0 degree 180 degree
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LIDAR Terminologies LIDAR Beams Scan Laser Beams Time Stamp Range
1 Scan = 180 beams 75 Scans per Sec
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LIDAR Mounting Parallel to Ground Used by CMU Minimum obstacle size
Slopes as obstacles Sensitive to Vibrations Mountings Air Pressure
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LIDAR Mounting Sweeping the terrain Scans sweep terrain
Successive scans are geographically close Consecutive scans on flat ground
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LIDAR Mounting Vertical Mounting Combination Mounting Team GRAY
Data Discontinuity Combination Mounting
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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
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Prior Work Review Dependent on Incremental scan sweeping
Flat terrain Sensor mountings Will not work if sensor mounting is changed
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Off-Road Conditions- Bumps
Indoor Vs. Off-road Environments Effect of Bumps 2 3 4 1 Scattered scans due to bumps
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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
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Sensor Stabilizers Cost
- The cost of the CMU Gimbal is approximately $70,000. Single Point of Failure
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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
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Core Algorithm Obstacle Detection Algorithm Theory
Implementation for a real time system – CajunBot Error Handling
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Algorithm Theory Points Slope Computation Triangle Formation
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Algorithm Theory (Continued)
High Absolute Slope High Relative Slope Height Discontinuity Obstacle Triangle Analysis
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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
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. . . . . . . . . . . . Real Time System Slope Computation Pos +
Position Pos + Orientation Data Fusion + (Range, Angle),75
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Effect of Bumps Scans Scattered due to bumps
Consecutive scans might be geographically far apart 2 3 4 1
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Spatial Griding .. . … Slope computation . . . . . . Data Fusion +
Position Position, Location . . . . . . .. . … Data Fusion + Slope Grid (Range, Angle),75
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Data Consistency Temporal accuracy of GPS GPS Drift 1 2 3 4
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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
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Handling GPS Drift Temporal Data Ordering GPS stable for 3-4 seconds
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Obstacle Detection Obstacle Cell Analysis Absolute Slope
Relative Slope Height Discontinuity Terrain Obstacle Map (TOM) Grid
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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
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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)
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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
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GPS Spike Data Playback
Effect of GPS Spike GPS Spike Data Playback Graph: GPS Z Vs. Time
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GPS Spike: Reason for False Obstacles
Corrupted data enters system Slope computation gets erroneous Data Filter
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Detecting GPS Spike Median filter monitors INS Data
Erroneous data is discarded
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Core Algorithm- Revised
Terrain Modeling Obstacle Detection
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Effect of Bumps - II INS, LIDAR data fusion -Mounting INS on LIDAR
-Good Suspensions -Sensor Stabilizers -Rigid Platform LIDARS GPS INS Rigid Platform
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Effects of Bumps INS, LIDAR Data Rate Mismatch LIDAR X Angle INS Time
X t2
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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
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Algorithm Evaluation Ability to utilize bumps to see further
Accuracy of results Algorithm complexity Scalability Different obstacle types Sensor orientation independence
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Data Sets Logged data from 2005 GC
Testing in a controlled environment with CajunBot-II Testing in a simulated environment - CBSim
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Evaluation – Effects of Bumps
Obstacle detection distance increases linearly with severity of bumps experienced
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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
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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
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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
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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
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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 %
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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.
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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
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Future Work Dynamic obstacle detection Obstacle Classification
Detecting trajectory and speed Obstacle Classification Vegetation, mesh, etc
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Questions ?
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Thank you
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Backup Slides
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Boresight Angles / Offsets
Hs 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
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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
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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
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Sensors Used LIDARs GPS INS
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GPS/INS Principle of Operation Data Format Erroneous Conditions
Position 5 Hz Position + Orientation GPS INS 100 Hz
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LIDARS Principle of Operation – Time of Flight
Data Format – 75 Hz at 0.25 degree offset Erroneous Conditions Laser Beams
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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
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Data Handling
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. . . . . . LIDAR Terminologies LIDAR Beams Laser Scan
1 scan = 180 beams 75 scans per sec
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