TY - GEN
T1 - Real-Time Visual Perception System for a Robotic Rat
AU - Jia, Guanglu
AU - Shi, Qing
AU - Chen, Chen
AU - Xu, Yi
AU - Li, Chang
AU - Ma, Mengchao
AU - Huang, Qiang
AU - Fukuda, Toshio
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/28
Y1 - 2020/9/28
N2 - The visual sensing ability of the robotic rat will be conducive to the biomimetic interaction between the robot and rats. However, most of the existing small-sized robotic rats do not have perception system, limiting their ability to perceive their surroundings or tracing the object. To address this problem, we developed a visual perception system for the robot, which included a control and drive system, a stereo camera integrated in the robot's head, and a wireless image transmission system. We also analyzed wheel odometry model and integrate it into the robot. The whole system can achieve real-Time data acquisition and processing. In the experimental scene, we extracted the number of dynamic rats feature points and analyzed the impact on environment modeling, and these feature points were easy to interfere with the robot's perception and modeling of the environment. Additionally, our system was capable of SLAM (Simultaneous Localization and Mapping) when tested by using the RTAB-Map (Real-Time Appearance-Based Mapping) algorithm. The experimental results showed that the trajectory was basically coherent and finally returns to the origin with less drift.
AB - The visual sensing ability of the robotic rat will be conducive to the biomimetic interaction between the robot and rats. However, most of the existing small-sized robotic rats do not have perception system, limiting their ability to perceive their surroundings or tracing the object. To address this problem, we developed a visual perception system for the robot, which included a control and drive system, a stereo camera integrated in the robot's head, and a wireless image transmission system. We also analyzed wheel odometry model and integrate it into the robot. The whole system can achieve real-Time data acquisition and processing. In the experimental scene, we extracted the number of dynamic rats feature points and analyzed the impact on environment modeling, and these feature points were easy to interfere with the robot's perception and modeling of the environment. Additionally, our system was capable of SLAM (Simultaneous Localization and Mapping) when tested by using the RTAB-Map (Real-Time Appearance-Based Mapping) algorithm. The experimental results showed that the trajectory was basically coherent and finally returns to the origin with less drift.
UR - http://www.scopus.com/inward/record.url?scp=85099382410&partnerID=8YFLogxK
U2 - 10.1109/RCAR49640.2020.9303325
DO - 10.1109/RCAR49640.2020.9303325
M3 - Conference contribution
AN - SCOPUS:85099382410
T3 - 2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020
SP - 93
EP - 98
BT - 2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020
Y2 - 28 September 2020 through 29 September 2020
ER -