TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Chehri, Abdellah
A1  - Zarai, Ahmed
A1  - Zimmermann, Alfred
A1  - Saadane, Rachid
ED  - Zimmermann, Alfred
ED  - Howlett, Robert
ED  - Jain, Lakhmi
ED  - Schmidt, Rainer
T1  - 2D autonomous robot localization using fast SLAM 2.0 and YOLO in long corridors
T2  - Human centred intelligent systems : proceedings of KES-HCIS 2021 conference ; Smart innovation, systems and technologies, volume 244
N2  - Autonomous navigation is one of the main areas of research in mobile robots and intelligent connected vehicles. In this context, we are interested in presenting a general view on robotics, the progress of research, and advanced methods related to this field to improve autonomous robots’ localization. We seek to evaluate algorithms and techniques that give robots the ability to move safely and autonomously in a complex and dynamic environment. Under these constraints, we focused our work in the paper on a specific problem: to evaluate a simple, fast and light SLAM algorithm that can minimize localization errors. We presented and validated a FastSLAM 2.0 system combining scan matching and loop closure detection. To allow the robot to perceive the environment and detect objects, we have studied one of the best deep learning technique using convolutional neural networks (CNN). We validate our testing using the YOLOv3 algorithm.
KW  - autonomous vehicles
KW  - SLAM
KW  - lidar sensor
KW  - deep learning
KW  - convolutional neural networks
Y1  - 2021
U6  - https://doi.org/10.1007/978-981-16-3264-8_19
DO  - https://doi.org/10.1007/978-981-16-3264-8_19
SP  - 199
EP  - 208
S1  - 10
PB  - Springer
CY  - Singapore
ER  -