Real-time Semantic Mapping for Autonomous Off-Road Navigation

Abstract

In this paper we describe a semantic mapping system for autonomous off-road driving with an All-Terrain Vehicle (ATVs). The system's goal is to provide a richer representation of the environment than a purely geometric map, allowing it to distinguish, e.g. tall grass from obstacles. The system builds a 2.5D grid map encoding both geometric(terrain height) and semantic information (navigation-relevant classes such as trail, grass, etc.). The geometric and semantic information are estimated online and in real-time from LiDAR and image sensor data, respectively. Using this semantic map, motion planners can create semantically aware trajectories. To achieve robust and efficient semantic segmentation, we design a custom Convolutional Neural Network (CNN) and train it with a novel dataset of labeled off-road imagery built for this purpose. We evaluate our semantic segmentation offline, showing comparable performance to the state of the art with slightly lower latency. We also show closed-loop field results with an autonomous ATV driving over challenging off-road terrain by using the semantic map in conjunction with a simple path planner. Our models and labeled dataset will be publicly available.