2026-09-20T00:44:07Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/109359 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

application/pdf http://hdl.handle.net/2142/109359 en Do, Minh N Lai, Andy 2021-03-05T21:36:56Z 2021-03-05T21:36:56Z 2020-11-16 2020-12 This thesis presents an object-level SLAM system capable of tracking objects in frame and classifying stationary and moving objects. The system combines two open-source algorithms, Mask R-CNN and ORB-SLAM. Mask R-CNN provides instance-level object detection and segmentation, while ORB-SLAM provides keypoint detection, camera tracking, and local mapping. A typical SLAM system assumes a static environment and treats dynamic objects in the scene as outliers. By using object-level information from Mask R-CNN, we extend the capability to recognize and track dynamic objects. The system uses only monocular images as input, resulting in numerous potentially low-cost applications without the need for calibrating multiple sensors or using a stereo rig. This system gives a mobile agent the capability of understanding and potentially interacting with its dynamic environment. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-03-04 without embargo terms The student, Andy Lai, accepted the attached license on 2020-11-13 at 13:24. The student, Andy Lai, submitted this Thesis for approval on 2020-11-13 at 13:27. This Thesis was approved for publication on 2020-11-16 at 11:38. DSpace SAF Submission Ingestion Package generated from Vireo submission #15883 on 2021-03-04 at 15:34:27 Made available in DSpace on 2021-03-05T21:36:56Z (GMT). No. of bitstreams: 2 LAI-THESIS-2020.pdf: 23155337 bytes, checksum: e6e664a8134f24494d67aa5330353990 (MD5) LICENSE.txt: 4205 bytes, checksum: 027e5c524ef4f511ee9f094086404927 (MD5) Previous issue date: 2020-11-16 Copyright 2020 Andy Lai Computer vision object recognition object tracking SLAM Dynamic object tracking and classification from a moving platform text Thesis Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Thesis M.S.