Off-Road Lidar Simulation with Data-Driven Terrain Primitives

Abstract

Developing software for large scale off-road robot applications is challenging and tedious due to cost, logistics, and rigor of field testing. High-fidelity sensor-realistic simulation can speed up the development process for perception and state estimation algorithms. We focus on Lidar simulation for robots operating in off-road environments. Lidars are integral sensors for robots, and Lidar simulation for off-road environments is particularly challenging due to the way Lidar rays interact with natural terrain such as vegetation. A hybrid geometric terrain representation has been shown to model Lidar observations well [1]. However, previous work has only been able to simulate a single, fixed scene, and the entire scene had to be precisely surveyed. In this work, we add semantic information to the hybrid geometric model. This allows us to extract terrain primitives, such as trees and shrubs, from data logs. Our approach uses these primitives to compose arbitrary scenes for Lidar simulation. We evaluate our simulator on a real-world environment of interest, and show that primitives derived using our approach generalize to new scenes.