High-performance Racing on Unmapped Tracks using Local Maps

Map-based methods for autonomous racing estimate the vehicle's location, which is used to follow a high-level plan. While map-based methods demonstrate high-performance results, they are limited by requiring a map of the environment. In contrast, mapless methods can operate in unmapped contexts since they directly process raw sensor data (often LiDAR) to calculate commands, but suffer from poor performance. In response, we propose the local map framework that uses easily extractable, low-level features to build local maps of the visible region that form the input to optimisation-based trajectory planners. Our local map generation extracts the visible racetrack boundaries and calculates a centre line and track widths used for planning. We evaluate our method for simulated F1Tenth autonomous racing using a trajectory optimisation and tracking strategy and a model predictive controller. Our method achieves lap times that are 8.8% faster than the Follow-The-Gap method and 3.22% faster than end-to-end neural networks due to the optimisation resulting in a faster speed profile. The local map planner is 3.28% slower than global methods that have access to an entire map of the track that can be used for planning. Critically, our approach enables high-speed autonomous racing on unmapped tracks, achieving performance similar to global methods without requiring a track map.