A SelfSupervised Terrain Roughness Estimator for OffRoad Autonomous Driving

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A Self-Supervised Terrain Roughness Estimator for
Off-Road Autonomous Driving

• David Stavens and Sebastian Thrun
• Stanford Artificial Intelligence Lab

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Self-Supervised Learning
Combines strengths of multiple sensors.
Ultra-Precise, No Range
Precise, Long Range
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Overview

• Introduction and Motivation
• Classifying Terrain Roughness
• Self-Supervised Learning
• Experimental Results

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2005 DARPA Grand Challenge
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Velocity Planning for DGC 2005

• Mobile robotics traditionally focuses on
  steering.
• But speed is also important.
• Beyond stopping distance and lateral
  maneuverability.
• 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.

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Velocity Planning for DGC 2005

• 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.
• Discontinuous control problem.
• Hard to solve with conventional control
  approaches.
• We used supervised learning.

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Experiments for DGC 05
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This Talk Next Logical Step

• We expand our online approach to be proactive.
• Our previous approach was entirely reactive.
• Difficult to be that precise with laser scanners.
• Hence problems of uncertainty and learning.
• Accuracy required for roughness detection exceeds
  that required for obstacle avoidance.
• 15cm vs. 2-4cm

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Other Approaches to Velocity Control

• Terramechanics guidance through rough terrain.
• Online assessment only at low speeds.
• High speeds require a priori maps.
• Our approach is both online and at high speeds.
• Speeds up to 35 mph.

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CMUs Preplanning Trailer
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Overview

• Introduction and Motivation
• Classifying Terrain Roughness
• Self-Supervised Learning
• Experimental Results

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Acquiring a 3D Point Cloud
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Errors in Pose and Projection
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Z Error vs. Time
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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.

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Comparing Two Laser Points

• ?pair
• ?1 ?z ?2
• ?3 ?t ?4
• ?5 xy distance ?6
• ?7 dpitch1 ?8 ?7 dpitch2 ?8
• ?9 droll1 ?10 ?9 droll2 ?10
• Seven Features ?z, ?t, xy distance, dpitches,
  drolls
• 10 Parameters ?1 ?2 ?10 (generated with
  self-supervised learning)

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Combining Multiple Comparisons

• 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 ?11i
• 
  i 0
• This generates a score, R, for that patch of
  terrain.
• But how do we assign target values to R?

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Overview

• Introduction and Motivation
• Classifying Terrain Roughness
• Self-Supervised Learning
• Experimental Results

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Self-Supervised Learning
Actual shock when driving over terrain modifies
belief about original laser scan. Improves
classifier for subsequent scans!
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Caveat Must Correct for Speed
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Mapping from R to Shock

• Learn a simple suspension model in parallel with
  the classifier
• Rcombined Rleft ?12 Rright ?12
• Rleft and Rright is for the terrain under each
  wheel.

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Overview

• Introduction and Motivation
• Classifying Terrain Roughness
• Self-Supervised Learning
• Experimental Results

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Summary

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

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• Questions?