Multisensor Data Fusion for Reliable Obstacle Avoidance

Thanh Nguyen Canh

Truong-Son Nguyen

Cong Hoang Quach

Xiem HoangVan

Manh Duong Phung

VNU University of Engineering and Technology, Vietnam
ICCAIS, 2022.

[Paper]

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.

Paper

Thanh Nguyen Canh, Truong Son Nguyen, Cong Hoang Quach, Xiem HoangVan, Manh Duong Phung

Multisensor Data Fusion for Reliable Obstacle Avoidance

ICCAIS 2022.

[pdf]

Overview and Results

Overview

Experiments

Citation

1. Canh T. N., Nguyen T. S., Quach C. H., HoangVan X., Phung, M. D. Multisensor Data Fusion for Reliable Obstacle Avoidance. International Conference on Control, Automation and Information Sciences (ICCAIS), 2022.

@inproceedings{canh2023object, 
                                  

                                    author    = {Canh, Thanh Nguyen and Nguyen, Truong Son and Quach, Cong Hoang and HoangVan, Xiem and Phung, Manh Duong}, 
                                  

                                    title     = {{Multisensor Data Fusion for Reliable Obstacle Avoidance}}, 
                                  

                                    booktitle = {2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)}, 
                                  

                                    year      = {2022}, 
                                  

                                    pages     = {385--390}, 
                                  

                                    organization={IEEE},
                                  

                                    DOI       = {10.1109/ICCAIS56082.2022.9990495}
                                  

                                }

Acknowledgements

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