Enhancing Body-Mounted LiDAR SLAM using an IMU-based Pedestrian Dead Reckoning (PDR) Model

With the reduction in motion sensors' cost and power, Simultaneous Localization and Mapping (SLAM) has emerged as a core technology in several applications such as search-and-rescue, first-responders, and defence. Existing SLAM methods were designed mainly for robotic platforms that use wheel odometry. However, wheel odometry is not available for body-mounted platforms. This thesis addresses the challenge of body-mounted SLAM by proposing an integrated sensor fusion scheme. A Pedestrian Dead Reckoning (PDR) model based on inertial sensors is used to enhance LiDAR-based SLAM. This proposed fusion uses the PDR model as a replacement for wheel odometry in vehicular platforms. A system prototype has been developed and used for data collection and experiments. The implemented PDR model was integrated into the Google Cartographer SLAM engine and tested against different tracking systems. Experiments demonstrated that the integration of PDR has significantly enhanced head-mounted SLAM accuracy leading to accurate positioning under different motion scenarios.

Name thesis:degree_name

Master of Applied Science (M.App.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering, Electrical and Computer

Grantor dc:publisher

Carleton University

Year dc:date.issued

2020

Author dc:creator

• Sadruddin, Hamza

Statement dc:rights

• Copyright © 2020 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2020-14304

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/43334