Multilayer occupancy grid for obstacle avoidance in an autonomous ground vehicle using RGB-D camera

TL;DR

This paper presents a multilayer occupancy grid system that integrates RGB-D camera data into an autonomous ground vehicle's navigation, improving obstacle detection with a 3D perception approach based on expanding traditional 2D LIDAR data.

Contribution

It introduces a novel multilayer costmap that combines RGB-D camera data with existing LIDAR information for enhanced obstacle avoidance in autonomous vehicles.

Findings

Enhanced obstacle detection with 3D perception

Integration of RGB-D camera into navigation system

Foundation for robust vision-based navigation

Abstract

This work describes the process of integrating a depth camera into the navigation system of a self-driving ground vehicle (SDV) and the implementation of a multilayer costmap that enhances the vehicle's obstacle identification process by expanding its two-dimensional field of view, based on 2D LIDAR, to a three-dimensional perception system using an RGB-D camera. This approach lays the foundation for a robust vision-based navigation and obstacle detection system. A theoretical review is presented and implementation results are discussed for future work.