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

TL;DR

This paper introduces a self-supervised machine learning method to estimate the second derivative of terrain roughness from laser data, enabling off-road vehicles to anticipate and reduce shock by slowing down on rough terrain.

Contribution

It presents a novel self-supervised approach for estimating terrain roughness second derivatives using laser and shock data, improving vehicle safety in off-road conditions.

Findings

The classifier accurately predicts high-roughness terrain sections.

The method reduces vehicle shock by enabling preemptive slowing.

It outperforms previous roughness estimation techniques.

Abstract

We present a machine learning approach for estimating the second derivative of a drivable surface, its roughness. Robot perception generally focuses on the first derivative, obstacle detection. However, the second derivative is also important due to its direct relation (with speed) to the shock the vehicle experiences. Knowing the second derivative allows a vehicle to slow down in advance of rough terrain. Estimating the second derivative 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 in projecting laser readings. This precision may be unavailable due to latency or error in the pose estimation. We model these sources of error as a multivariate polynomial. Its coefficients are learned using the shock data as ground truth -- the accelerometers are used to train the lasers. The resulting classifier operates on individual laser readings from a road surface described by a 3D point cloud. The classifier identifies sections of road where the second derivative is likely to be large. Thus, the vehicle can slow down in advance, reducing the shock it experiences. The algorithm is an evolution of one we used in the 2005 DARPA Grand Challenge. We analyze it using data from that route.