Multisensor Data Fusion for Reliable Obstacle Avoidance

TL;DR

This paper presents a multisensor data fusion method combining depth cameras and LiDAR, using projection techniques and a dynamic window approach to enable robots to reliably avoid static and dynamic obstacles in various environments.

Contribution

A novel multisensor fusion framework integrating depth cameras and LiDAR with a projection technique for improved obstacle avoidance in robots.

Findings

Effective avoidance of static obstacles demonstrated

Successful dynamic obstacle navigation shown

Enhanced environmental perception achieved

Abstract

In this work, we propose a new approach that combines data from multiple sensors for reliable obstacle avoidance. The sensors include two depth cameras and a LiDAR arranged so that they can capture the whole 3D area in front of the robot and a 2D slide around it. To fuse the data from these sensors, we first use an external camera as a reference to combine data from two depth cameras. A projection technique is then introduced to convert the 3D point cloud data of the cameras to its 2D correspondence. An obstacle avoidance algorithm is then developed based on the dynamic window approach. A number of experiments have been conducted to evaluate our proposed approach. The results show that the robot can effectively avoid static and dynamic obstacles of different shapes and sizes in different environments.