Learn to Navigate Maplessly with Varied LiDAR Configurations: A Support Point-Based Approach

TL;DR

This paper introduces a support point-based deep reinforcement learning model for mapless navigation that can adapt to various LiDAR configurations and installation positions, demonstrating robust performance in diverse scenarios.

Contribution

It presents a novel DRL navigation approach that processes variable-range sensor data using support points, enabling flexible sensor configurations and improved obstacle avoidance.

Findings

Effective handling of different LiDAR setups.

Strong performance in simulation and real-world tests.

Enhanced navigation in crowded environments with high-resolution LiDAR.

Abstract

Deep reinforcement learning (DRL) demonstrates great potential in mapless navigation domain. However, such a navigation model is normally restricted to a fixed configuration of the range sensor because its input format is fixed. In this paper, we propose a DRL model that can address range data obtained from different range sensors with different installation positions. Our model first extracts the goal-directed features from each obstacle point. Subsequently, it chooses global obstacle features from all point-feature candidates and uses these features for the final decision. As only a few points are used to support the final decision, we refer to these points as support points and our approach as support point-based navigation (SPN). Our model can handle data from different LiDAR setups and demonstrates good performance in simulation and real-world experiments. Moreover, it shows great potential in crowded scenarios with small obstacles when using a high-resolution LiDAR.