Building Large Scale Traversability Maps Using Vehicle Experience

Abstract

Traversability maps are a global spatial representation of the relative difficulty in driving through a local region. These maps support simple optimisation of robot paths and have been very popular in path planning techniques. Despite the popularity of these maps, the methods for generating global traversability maps have been limited to using a-priori information. This paper explores the construction of large scale traversability maps for a vehicle performing a repeated activity in a bounded working environment, such as a repeated delivery task. We evaluate the use of vehicle power consumption, longitudinal slip, lateral slip and vehicle orientation to classify the traversability and incorporate this into a map generated from sparse information.