%A Van Lien Tran
%A Viet Thang Nguyen
%A Cong Hoang Quach
%A Xuan Hieu Phan
%A Minh Trien Pham
%T Depth camera based navigation algorithms for indoor mobile robot
%X How can we efficiently search an object in a room? This report introduces a method for a single indoor mobile robot to find a hidden item based on states of the room when the robot is moving. A 2D distribution, called cognitive map, is built during robot movements to boost the exploring time. It is known that in the filed of exploring algorithms, A∗ usually takes more time to reach the target than recent invented algorithms such as rapidly-exploring random trees (RRT) and probabilistic roadmap (PRM). However, by adapting the cognitive map as a cost map, the A∗ algorithm is significantly improved and surpasses the two algorithms in Scannet 3D dataset. We also introduce application of depth sensors and SLAM solvers on reconstructing the room and updating cognitive map. By running a virtual robot in Gazebo simulator, it is proved that our method can work well on synthetic environment and hence, is very promising to be worked on real-life environment.
%D 2018
%I VNU-UET
%R TR2018-FIT-16
%L SisLab3195