eprintid: 3195
rev_number: 8
eprint_status: archive
userid: 290
dir: disk0/00/00/31/95
datestamp: 2018-12-08 08:49:26
lastmod: 2018-12-08 08:49:26
status_changed: 2018-12-08 08:49:26
type: monograph
metadata_visibility: show
creators_name: Tran, Van Lien
creators_name: Nguyen, Viet Thang
creators_name: Quach, Cong Hoang
creators_name: Phan, Xuan Hieu
creators_name: Pham, Minh Trien
creators_id: hoangqc@vnu.edu.vn
creators_id: hieupx@vnu.edu.vn
creators_id: trienpm@vnu.edu.vn
title: Depth camera based navigation algorithms for indoor mobile robot
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: 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.
date: 2018-12
date_type: completed
publisher: VNU-UET
id_number: TR2018-FIT-16
full_text_status: public
monograph_type: technical_report
pages: 10
institution: VNU University of Engineering and Technology
department: Faculty of Information Technology
citation:   Tran, Van Lien and Nguyen, Viet Thang and Quach, Cong Hoang and Phan, Xuan Hieu and Pham, Minh Trien  (2018) Depth camera based navigation algorithms for indoor mobile robot.  Technical Report. VNU-UET.     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3195/1/FIT-TR2018-TranVanLien-PhanXuanHieu.pdf