A Vision-based Irregular Obstacle Avoidance Framework via Deep Reinforcement Learning

TL;DR

This paper introduces a vision-based collision avoidance framework using deep reinforcement learning that combines depth and semantic information from RGB images to effectively navigate around irregular obstacles, outperforming laser-based methods.

Contribution

The paper proposes a novel pseudo-laser data approach that fuses visual and semantic information, enhancing irregular obstacle avoidance in real-world scenarios.

Findings

Achieves state-of-the-art performance in unseen virtual scenarios.

Demonstrates robustness in real-world obstacle avoidance.

Improves irregular obstacle handling over traditional laser-based methods.

Abstract

Deep reinforcement learning has achieved great success in laser-based collision avoidance work because the laser can sense accurate depth information without too much redundant data, which can maintain the robustness of the algorithm when it is migrated from the simulation environment to the real world. However, high-cost laser devices are not only difficult to apply on a large scale but also have poor robustness to irregular objects, e.g., tables, chairs, shelves, etc. In this paper, we propose a vision-based collision avoidance framework to solve the challenging problem. Our method attempts to estimate the depth and incorporate the semantic information from RGB data to obtain a new form of data, pseudo-laser data, which combines the advantages of visual information and laser information. Compared to traditional laser data that only contains the one-dimensional distance information captured at a certain height, our proposed pseudo-laser data encodes the depth information and semantic information within the image, which makes our method more effective for irregular obstacles. Besides, we adaptively add noise to the laser data during the training stage to increase the robustness of our model in the real world, due to the estimated depth information is not accurate. Experimental results show that our framework achieves state-of-the-art performance in several unseen virtual and real-world scenarios.