1. A Self-Supervised Terrain Roughness Estimator for Off-Road Autonomous Driving David Stavens and Sebastian Thrun Stanford Artificial Intelligence Lab

2. David Stavens, Sebastian Thrun Self-Supervised Learning “Combines” strengths of multiple sensors. Ultra-Precise, No RangePrecise, Long Range

3. David Stavens, Sebastian Thrun Overview n Introduction and Motivation n Classifying Terrain Roughness n Self-Supervised Learning n Experimental Results

4. David Stavens, Sebastian Thrun 2005 DARPA Grand Challenge

5. David Stavens, Sebastian Thrun Velocity Planning for DGC 2005 n Mobile robotics traditionally focuses on steering. n But speed is also important. –Beyond stopping distance and lateral maneuverability. n For Grand Challenge 2005, our vehicle adapted its speed to terrain conditions, minimizing shock: –Increases electrical and mechanical reliability. –Mitigates pose error for laser projection. –Increases traction for improved maneuvers. –Seems to be correlated with slowing on “hard” terrain.

6. David Stavens, Sebastian Thrun Velocity Planning for DGC 2005 n Simple three state algorithm: –Drive at speed limit until shock threshold exceeded. –Slow to bring the vehicle within the shock threshold. Uses approx. linear relationship between shock and speed. Which is also important for the new work we present. –Accelerate back to the speed limit. n Discontinuous control problem. –Hard to solve with conventional control approaches. n We used supervised learning.

7. David Stavens, Sebastian Thrun Experiments for DGC 05

8. David Stavens, Sebastian Thrun This Talk: Next Logical Step n We expand our online approach to be proactive. –Our previous approach was entirely reactive. n Difficult to be that precise with laser scanners. –Hence problems of uncertainty and learning. n Accuracy required for roughness detection exceeds that required for obstacle avoidance. –15cm vs. 2-4cm

9. David Stavens, Sebastian Thrun Other Approaches to Velocity Control n Terramechanics: guidance through rough terrain. –Online assessment only at low speeds. –High speeds require a priori maps. n Our approach is both online and at high speeds. –Speeds up to 35 mph.

10. David Stavens, Sebastian Thrun CMU’s Preplanning Trailer

11. David Stavens, Sebastian Thrun Overview n Introduction and Motivation n Classifying Terrain Roughness n Self-Supervised Learning n Experimental Results

12. David Stavens, Sebastian Thrun Acquiring a 3D Point Cloud

13. David Stavens, Sebastian Thrun Errors in Pose and Projection

14. David Stavens, Sebastian Thrun Z Error vs. Time

15. David Stavens, Sebastian Thrun More than  t “Spread” of plot implies more factors than  t.  t is also related to: –Amount/rate of pitching. –Distance between the two scans.

16. David Stavens, Sebastian Thrun Comparing Two Laser Points  pair =  1 |  z |  2 –  3 |  t |  4 –  5 | xy distance |  6 –  7 | dpitch 1 |  8 –  7 | dpitch 2 |  8 –  9 | droll 1 |  10 –  9 | droll 2 |  10 Seven Features:  z,  t, xy distance, dpitches, drolls 10 Parameters:  1  2 …  10 (generated with self-supervised learning)

17. David Stavens, Sebastian Thrun Combining Multiple Comparisons n n pairs in ascending order. –Use weighting because resolution of discontinuities is near resolution of laser. There are not many witness pairs. n R =   pair  11 i i = 0 n This generates a score, R, for that patch of terrain. n But how do we assign target values to R?

18. David Stavens, Sebastian Thrun Overview n Introduction and Motivation n Classifying Terrain Roughness n Self-Supervised Learning n Experimental Results

19. David Stavens, Sebastian Thrun Self-Supervised Learning Actual shock when driving over terrain modifies belief about original laser scan. Improves classifier for subsequent scans!

20. David Stavens, Sebastian Thrun Caveat: Must Correct for Speed

21. David Stavens, Sebastian Thrun Mapping from R to Shock Learn a simple suspension model in parallel with the classifier: R combined = R left  12 + R right  12 R left and R right is for the terrain under each wheel.

22. David Stavens, Sebastian Thrun Overview n Introduction and Motivation n Classifying Terrain Roughness n Self-Supervised Learning n Experimental Results

23. David Stavens, Sebastian Thrun

25. Summary n Road shock provides ground truth for previously perceived patches of road. n Perception model improves in real-time. n Future terrain assessment is more precise. n A faster route completion time is possible. –For the same amount of shock. n Works either “offline” or “as you drive.” –Offline results presented.

26. David Stavens, Sebastian Thrun Questions?