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

<p>The novel framework for estimating dense scene flow using depth camera data is demonstrated in the article. Using these estimated flow vectors to identify obstacles improves the path planning module of the autonomous vehicle's (AV) intelligence. The primary difficulty in the development of AVs has been thought to be path planning in cluttered environments. These vehicles must possess the intelligence to recognize their surroundings and successfully navigate around obstacles. The AV needs a thorough understanding of the surroundings to detect and avoid obstacles in a cluttered environment. Therefore, when determining the course, it is preferable to be aware of the kinematic behavior (position and the direction) of the obstacles. As a result, by comparing the depth images between different time frames, the position and direction of the obstacles are calculated using a 3D vision sensor. The current study focuses on the extraction of the flow vectors in 3D coordinates from the differential scene flow method. Generally, the evaluation of scene flow algorithms is crucial in determining their accuracy and effectiveness in different applications. The gradient of the vector field snake model, which extracts changes in pixel values in three directions, is combined with the scene flow technique to identify both static and dynamic obstacles. Our goal is to create a single-vision sensor-based real-time obstacle avoidance method based on scene flow estimation. In addition, the common evaluation metrics such as endpoint error (EPE), average angular error (AAE), and standard deviation angular error (STDAE) are used to measure the accuracy of different algorithms in terms of computational errors with the benchmark Middlebury datasets. The proposed technique is validated with different experiments using a Pixel-Mixed-Device (PMD) camera and a Kinect sensor as 3D sensors. Finally, the numerical and experimental results are displayed and reported.</p>
</abstract>

The novel framework for estimating dense scene flow using depth camera data is demonstrated in the article. Using these estimated flow vectors to identify obstacles improves the path planning module of the autonomous vehicle's (AV) intelligence. The primary difficulty in the development of AVs has been thought to be path planning in cluttered environments. These vehicles must possess the intelligence to recognize their surroundings and successfully navigate around obstacles. The AV needs a thorough understanding of the surroundings to detect and avoid obstacles in a cluttered environment. Therefore, when determining the course, it is preferable to be aware of the kinematic behavior (position and the direction) of the obstacles. As a result, by comparing the depth images between different time frames, the position and direction of the obstacles are calculated using a 3D vision sensor. The current study focuses on the extraction of the flow vectors in 3D coordinates from the differential scene flow method. Generally, the evaluation of scene flow algorithms is crucial in determining their accuracy and effectiveness in different applications. The gradient of the vector field snake model, which extracts changes in pixel values in three directions, is combined with the scene flow technique to identify both static and dynamic obstacles. Our goal is to create a single-vision sensor-based real-time obstacle avoidance method based on scene flow estimation. In addition, the common evaluation metrics such as endpoint error (EPE), average angular error (AAE), and standard deviation angular error (STDAE) are used to measure the accuracy of different algorithms in terms of computational errors with the benchmark Middlebury datasets. The proposed technique is validated with different experiments using a Pixel-Mixed-Device (PMD) camera and a Kinect sensor as 3D sensors. Finally, the numerical and experimental results are displayed and reported.

Dense Scene Flow Estimation with the GVF Snake Model for Obstacle Detection Using a 3D Sensor in the Path-Planning Module

School of Engineering and Information Technology

International Journal on Smart Sensing and Intelligent Systems

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

The novel framework for estimating dense scene flow using depth camera data is demonstrated in the article. Using these estimated flow vectors to identify...

Dense Scene Flow Estimation with the GVF Snake Model for Obstacle Detection Using a 3D Sensor in the Path-Planning Module

Article Category: Research Article

Publicado en línea: 20 nov 2023

Páginas: -

Recibido: 01 may 2023

DOI: https://doi.org/10.2478/ijssis-2023-0015

Palabras clave
Scene Flow, Obstacle avoidance, 3D vision sensor, path planning

© 2023 Sobers Francis et al., published by Sciendo

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

Dense Scene Flow Estimation with the GVF Snake Model for Obstacle Detection Using a 3D Sensor in the Path-Planning Module

Article Category: Research Article

Publicado en línea: 20 nov 2023

Páginas: -

Recibido: 01 may 2023

DOI: https://doi.org/10.2478/ijssis-2023-0015

Palabras claveScene Flow, Obstacle avoidance, 3D vision sensor, path planning

© 2023 Sobers Francis et al., published by Sciendo

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

Palabras clave
Scene Flow, Obstacle avoidance, 3D vision sensor, path planning