Stereo Based 3D Perception for Obstacle Avoidance in Autonomous Wheelchair Navigation

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Gomes, B (Author) Other; Torres, J (Author) Other; Sobral, P (Author) Other; Armando Jorge Sousa (Author) FEUP; reis, lp (Author) FEUP

Conference proceedings International

Title: Lecture Notes in Networks and Systems

Pages: 321-332

5th Iberian Robotics Conference, ROBOT 2022

23 November 2022 through 25 November 2022

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: P-00X-FZJ

DOI: 10.1007/978-3-031-21065-5_27

Abstract (EN): In recent years, scientific and technological advances in robotics, have enabled the development of disruptive solutions for human interaction with the real world. In particular, the application of robotics to support people with physical disabilities, improved their life quality with a high social impact. This paper presents a stereo image based perception solution for autonomous wheelchairs navigation. It was developed to extend the Intellwheels project, a development platform for intelligent wheelchairs. The current version of Intellwheels relies on a planar scanning sensor, the Laser Range Finder (LRF), to detect the surrounding obstacles. The need for robust navigation capabilities means that the robot is required to precept not only obstacles but also bumps and holes on the ground. The proposed stereo-based solution, supported in passive stereo ZED cameras, was evaluated in a 3D simulated world scenario designed with a challenging floor. The performance of the wheelchair navigation with three different configurations was compared: first, using a LRF sensor, next with an unfiltered stereo camera and finally, applying a stereo camera with a speckle filter. The LRF solution was unable to complete the planned navigation. The unfiltered stereo camera completed the challenge with a low navigation quality due to noise. The filtered stereo camera reached the target position with a nearly optimal path.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12