<abstract xmlns="http://www.w3.org/1999/xhtml">

To address the problems of poor accuracy of traditional EKF algorithm in estimating the position of unmanned vehicles and the deficiencies in accuracy and map completeness of the traditional map building method with single-line LiDAR, this paper proposes a method to create fused raster maps realized with multi-source data. Firstly, the combined data of the inertial measurement unit and wheel encoder are corrected by adding the positional information output from the visual odometer using the error-state SLAM algorithm, and the local raster constructed by LiDAR and depth camera is fused frame by frame using the idea of Bayesian estimation to finally generate the fused global map. Then, a four-wheeled mobile unmanned vehicle with a LiDAR sensor and depth camera is selected as the experimental object, and dynamic environment avoidance simulation experiments are conducted to draw conclusions. The simulation experiment results show that when <italic>γ</italic> = 5.99, the algorithm generates a new local target point <italic>p</italic>g2 (17.49, 13.49) and the corresponding getaway path and finally guides the unmanned vehicle to the specified target point, verifying that the method in this paper can achieve the avoidance capability of the unmanned vehicle in the process of getting away from the newly emerged obstacles. This study uses the scanned data of LiDAR for the estimation of the real-time position of the unmanned vehicle to realize obstacle avoidance and path planning of the unmanned vehicle.
</abstract>

To address the problems of poor accuracy of traditional EKF algorithm in estimating the position of unmanned vehicles and the deficiencies in accuracy and map completeness of the traditional map building method with single-line LiDAR, this paper proposes a method to create fused raster maps realized with multi-source data. Firstly, the combined data of the inertial measurement unit and wheel encoder are corrected by adding the positional information output from the visual odometer using the error-state SLAM algorithm, and the local raster constructed by LiDAR and depth camera is fused frame by frame using the idea of Bayesian estimation to finally generate the fused global map. Then, a four-wheeled mobile unmanned vehicle with a LiDAR sensor and depth camera is selected as the experimental object, and dynamic environment avoidance simulation experiments are conducted to draw conclusions. The simulation experiment results show that when γ = 5.99, the algorithm generates a new local target point pg2 (17.49, 13.49) and the corresponding getaway path and finally guides the unmanned vehicle to the specified target point, verifying that the method in this paper can achieve the avoidance capability of the unmanned vehicle in the process of getting away from the newly emerged obstacles. This study uses the scanned data of LiDAR for the estimation of the real-time position of the unmanned vehicle to realize obstacle avoidance and path planning of the unmanned vehicle.

Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion

Nanjing University of Information Science & Technology

Quanzhou University of Information Engineering

Nanjing University of Aeronautics & Astronautics

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

To address the problems of poor accuracy of traditional EKF algorithm in estimating the position of unmanned vehicles and the deficiencies in accuracy and map...

Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion

Publicado en línea: 11 oct 2023

Páginas: -

Recibido: 10 ene 2023

Aceptado: 21 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00575

Palabras clave
Lidar, depth camera, Raster map method, Unmanned vehicle, Obstacle avoidance technique, Positional attitude, Bayesian estimation

© 2023 Hao Qiu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion

Publicado en línea: 11 oct 2023

Páginas: -

Recibido: 10 ene 2023

Aceptado: 21 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00575

Palabras claveLidar, depth camera, Raster map method, Unmanned vehicle, Obstacle avoidance technique, Positional attitude, Bayesian estimation

© 2023 Hao Qiu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Lidar, depth camera, Raster map method, Unmanned vehicle, Obstacle avoidance technique, Positional attitude, Bayesian estimation