Semantic map for indoor positioning system (floorplan enhancement with semantic SLAM)

• Main Author: Lim, Han Quan
• Other Authors: Lam Siew Kei
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
• Online Access: https://hdl.handle.net/10356/147869
• Institution: Nanyang Technological University

In navigation systems, the absence of GPS data poses an interesting challenge when it comes to managing drift. Systems such as ORB-SLAM manages this by performing Bundle Adjustment on loop closures, as such relatively accurate point-cloud maps may be generated from simple visual input only. In combination with semantic segmentation, semantically labelled point clouds are possible. A further visual enhancement may be made by comparing with a ground truth floorplan; not everything may be labelled in the floorplan and detections derived from the semantic cloud may be used for floorplan enhancement. Together, semantic slam and a ground truth floorplan may deliver a more visually appealing and accurate navigation visualisation. This project thus forms part of a system used to display a user’s position on a floorplan, as well as populate additional detections onto the floorplan using semantic SLAM by focusing on the frame matching problem between the machine generated semantically labelled octomap frame, and the floorplan frame.