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

Accurate perception is a principal challenge of autonomous off-road driving. Perceptive technologies generally focus on obstacle avoidance. However, at high speed, terrain roughness is also important to control shock the vehicle experiences. The accuracy required to detect rough terrain is significantly greater than that necessary for obstacle avoidance. We present a self-supervised machine learning approach for estimating terrain roughness from laser range data. Our approach compares sets of nearby surface points acquired with a laser. This comparison is challenging due to uncertainty. For example, at range, laser readings may be so sparse that significant information about the surface is missing. Also, a high degree of precision is required when projecting laser readings. This precision may be unavailable due to latency or error in pose estimation. We model these sources of error as a multivariate polynomial. The coefficients of this polynomial are obtained through a self-supervised learning process. The “labels” of terrain roughness are automatically generated from actual shock, measured when driving over the target terrain. In this way, the approach provides its own training labels. It “transfers” the ability to measure terrain roughness from the vehicle’s inertial sensors to its range sensors. Thus, the vehicle can slow before hitting rough terrain. Our experiments use data from the 2005 DARPA Grand Challenge. We find our approach is substantially more effective at identifying rough surfaces and assuring vehicle safety than previous methods – often by as much as 50%.