Occupancy Grid Based Reactive Planner

TL;DR

This paper presents a perception and path planning pipeline using occupancy grids and LiDAR for autonomous racing in unknown courses, achieving reliable high-speed laps and first-place finishes in competitions.

Contribution

It introduces a novel occupancy grid-based reactive planning method integrated with LiDAR perception for autonomous racing in unknown environments.

Findings

Achieved an average speed of 6.85 m/s over 434.2 meters.

Completed laps with an average time of 63.4 seconds.

Successfully finished in first place in competitions.

Abstract

This paper proposes a perception and path planning pipeline for autonomous racing in an unknown bounded course. The pipeline was initially created for the 2021 evGrandPrix autonomous division and was further improved for the 2022 event, both of which resulting in first place finishes. Using a simple LiDAR-based perception pipeline feeding into an occupancy grid based expansion algorithm, we determine a goal point to drive. This pipeline successfully achieved reliable and consistent laps in addition with occupancy grid algorithm to know the ways around a cone-defined track with an averaging speeds of 6.85 m/s over a distance 434.2 meters for a total lap time of 63.4 seconds.