TY - GEN
T1 - ORBSLAM2 and Point Cloud Processing towards Autonomous Underwater Robot Navigation
AU - Hidalgo, Franco
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/5
Y1 - 2020/10/5
N2 - Autonomous navigation for underwater robots is often approached from the integration of sophisticated imagery and dead-reckoning sensors to achieve a practical implementation. Image processing has become popular in robotics applications for autonomous navigation from object recognition, visual odometry to visual Simultaneous Localization and Mapping (vSLAM). In this paper, a minimal instrumentation setup is proposed towards autonomous navigation for underwater robots based on monocular ORBSLAM2 and Point Cloud Processing in structured environments. ORBSLAM2 is a vSLAM algorithm that generates a point cloud map of Oriented FAST and Rotated BRIEF (ORB) features from video images and localizes the video source in it. We evaluate the feasibility of the point cloud processing in the implementation of a basic navigation method. The cloud is processed to abstract the surroundings into 3D planes where a trajectory (i.e. transects) is easily generated, and a way-point robot controller is capable of driving the robot given the current location as feedback. The basis of the method is tested in a simulated environment.
AB - Autonomous navigation for underwater robots is often approached from the integration of sophisticated imagery and dead-reckoning sensors to achieve a practical implementation. Image processing has become popular in robotics applications for autonomous navigation from object recognition, visual odometry to visual Simultaneous Localization and Mapping (vSLAM). In this paper, a minimal instrumentation setup is proposed towards autonomous navigation for underwater robots based on monocular ORBSLAM2 and Point Cloud Processing in structured environments. ORBSLAM2 is a vSLAM algorithm that generates a point cloud map of Oriented FAST and Rotated BRIEF (ORB) features from video images and localizes the video source in it. We evaluate the feasibility of the point cloud processing in the implementation of a basic navigation method. The cloud is processed to abstract the surroundings into 3D planes where a trajectory (i.e. transects) is easily generated, and a way-point robot controller is capable of driving the robot given the current location as feedback. The basis of the method is tested in a simulated environment.
KW - cloud point
KW - ORBSLAM2
KW - underwater navigation
KW - Underwater robots
UR - http://www.scopus.com/inward/record.url?scp=85104673236&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF38699.2020.9389096
DO - 10.1109/IEEECONF38699.2020.9389096
M3 - Contribución a la conferencia
AN - SCOPUS:85104673236
T3 - 2020 Global Oceans 2020: Singapore - U.S. Gulf Coast
BT - 2020 Global Oceans 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020
Y2 - 5 October 2020 through 30 October 2020
ER -