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Mobile Robot Localization Evaluations with Visual Odometry in Varying Environments Using Festo-Robotino
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
Annotation ability available
Autonomous ground vehicles can use a variety of techniques to navigate the environment and deduce their motion and location from sensory inputs. Visual Odometry can provide a means for an autonomous vehicle to gain orientation and position information from camera images recording frames as the vehicle moves. This is especially useful when global positioning system (GPS) information is unavailable, or wheel encoder measurements are unreliable. Feature-based visual odometry algorithms extract corner points from image frames, thus detecting patterns of feature point movement over time. From this information, it is possible to estimate the camera, i.e., the vehicle’s motion. Visual odometry has its own set of challenges, such as detecting an insufficient number of points, poor camera setup, and fast passing objects interrupting the scene. This paper investigates the effects of various disturbances on visual odometry. Moreover, it discusses the outcomes of several experiments performed utilizing the Festo-Robotino robotic platform. The experiments are designed to evaluate how changing the system’s setup will affect the overall quality and performance of an autonomous driving system. Environmental effects such as ambient light, shadows, and terrain are also investigated. Finally, possible improvements including varying camera options and programming methods are discussed.
CitationAbdo, A., Ibrahim, R., and Rawashdeh, N., "Mobile Robot Localization Evaluations with Visual Odometry in Varying Environments Using Festo-Robotino," SAE Technical Paper 2020-01-1022, 2020, https://doi.org/10.4271/2020-01-1022.
- Aqel , M.O. , Marhaban , M.H. , Saripan , M.I. , and Ismail , N.B. Review of Visual Odometry: Types, Approaches, Challenges, and Applications Springerplus 5 1 1897 2016
- Maimone , M. , et al. 2007
- Yousif , K. , Bab-Hadiashar , A. , and Hoseinnezhad , R. Intell Ind Syst 1 289 2015 https://doi.org/10.1007/s40903-015-0032-7
- Bischoff , B. , et al. Fusing Vision and Odometry for Accurate Indoor Robot Localization 12th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE 2012
- Garibeh , M.H. , Jaradat , M.A.K. , and Rawashdeh , N.A. A Potential Field Simulation Study for Mobile Robot Path Planning in Dynamic Environments 2019 20th International Conference on Research and Education in Mechatronics (REM) Wels, Austria 2019 1 8
- Aladem , M. , Rawashdeh , S. , and Rawashdeh , N. Evaluation of a Stereo Visual Odometry Algorithm for Passenger Vehicle Navigation SAE Technical Paper 2017-01-0046 2017 https://doi.org/10.4271/2017-01-0046
- Rawashdeh , N. , Aladem , M. , Baek , S. , and Rawashdeh , S. Scene Structure Classification as Preprocessing for Feature-Based Visual Odometry SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 11 3 231 239 2018 https://doi.org/10.4271/2018-01-0610
- Negenborn , R. 2003
- Baatar , G. , Eichhorn , M. , and Ament , C. Precise Indoor Localization of Multiple Mobile Robots with Adaptive Sensor Fusion Using Odometry and Vision Data IFAC Proceedings Volumes 47 3 7182 7189 2014
- Mao , Y.F. , Wiedmann , H. , and Chen , M. Autonomous Mobile Robots and Development of Vision Based Automotive Assistance Systems Applied Mechanics and Materials 121 2012
- Robotino® https://www.festo-didactic.com/int-en/ November 3, 2019
- http://wiki.openrobotino.org/ 2019
- Oskiper , T. , Zhu , Z. , Samarasekera , S. and Kumar , R. Visual Odometry System Using Multiple Stereo Cameras and Inertial Measurement Unit 2007 IEEE Conference on Computer Vision and Pattern Recognition Minneapolis, MN 2007 1 8