Large scale localization and mapping for deployment of robotic wheelchair in a building

Gmapping and AMCL is a popular SLAM and localization combination in ROS1 that is used for the deployment of autonomous mobile robots. However, Gmapping is not suited for large environments as the mapping process has to be restarted from scratch and completed in one run. Furthermore, the mapping p...

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Main Author: Lim, Jit Ern
Other Authors: Ang Wei Tech
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
ROS
Online Access:https://hdl.handle.net/10356/176826
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1768262024-05-25T16:49:49Z Large scale localization and mapping for deployment of robotic wheelchair in a building Lim, Jit Ern Ang Wei Tech School of Mechanical and Aerospace Engineering Rehabilitation Research Institute of Singapore (RRIS) WTAng@ntu.edu.sg Computer and Information Science Engineering SLAM Localization Mapping Sensors Mobile robot Robotics ROS Gmapping and AMCL is a popular SLAM and localization combination in ROS1 that is used for the deployment of autonomous mobile robots. However, Gmapping is not suited for large environments as the mapping process has to be restarted from scratch and completed in one run. Furthermore, the mapping process has to be repeated once they are significant changes in the environment to maintain the localization accuracy of AMCL. Therefore, researchers have developed multisession and lifelong SLAM approaches for real-world operations in large environments. However for the deployment of a robotic wheelchair, where cost is also an important criteria along with safety, it is not clear which multisession and lifelong SLAM and localization method and sensor configuration is most suitable. In this paper, state-ofthe-art SLAM with lifelong and multisession functions are reviewed through literature to select a suitable approach. For our specific application, RTAB-Map is selected because it can receive inputs from multiple sensor combinations. It is subsequently used to determine the sensors required by performing mapping and localization in two large environments that are indoor and semi-outdoor. The hardware utilized is a robotic wheelchair equipped with these sensors: 2D LiDAR, 3D LiDAR, three RGB-D cameras, wheel encoders and IMU. For mapping, the quality of 2D occupancy grids is evaluated based on the geometry of the map and the representation of obstacles in the map to reduce the amount of manual postprocessing required. The sensor combination that created the best quality map is then changed to cheaper sensor subsets and compared among each other during localization to generate a guideline for sensor selection based on cost constraints and localization performance requirements. Then, the fused wheel encoder and IMU odometry is determine to be necessary and cannot be removed to further reduce cost after comparing their performance with exteroceptive odometry. Finally, the deployment of robotic wheelchair in a large environment is made more feasible due to the reduced cost using RTAB-Map with 3D LiDAR and two RGB-D cameras for mapping and RTAB-Map with 2D LiDAR and one RGB-D camera for localization with fused wheel encoder and IMU odometry for both cases. Bachelor's degree 2024-05-20T06:43:52Z 2024-05-20T06:43:52Z 2024 Final Year Project (FYP) Lim, J. E. (2024). Large scale localization and mapping for deployment of robotic wheelchair in a building. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176826 https://hdl.handle.net/10356/176826 en C005 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Engineering
SLAM
Localization
Mapping
Sensors
Mobile robot
Robotics
ROS
spellingShingle Computer and Information Science
Engineering
SLAM
Localization
Mapping
Sensors
Mobile robot
Robotics
ROS
Lim, Jit Ern
Large scale localization and mapping for deployment of robotic wheelchair in a building
description Gmapping and AMCL is a popular SLAM and localization combination in ROS1 that is used for the deployment of autonomous mobile robots. However, Gmapping is not suited for large environments as the mapping process has to be restarted from scratch and completed in one run. Furthermore, the mapping process has to be repeated once they are significant changes in the environment to maintain the localization accuracy of AMCL. Therefore, researchers have developed multisession and lifelong SLAM approaches for real-world operations in large environments. However for the deployment of a robotic wheelchair, where cost is also an important criteria along with safety, it is not clear which multisession and lifelong SLAM and localization method and sensor configuration is most suitable. In this paper, state-ofthe-art SLAM with lifelong and multisession functions are reviewed through literature to select a suitable approach. For our specific application, RTAB-Map is selected because it can receive inputs from multiple sensor combinations. It is subsequently used to determine the sensors required by performing mapping and localization in two large environments that are indoor and semi-outdoor. The hardware utilized is a robotic wheelchair equipped with these sensors: 2D LiDAR, 3D LiDAR, three RGB-D cameras, wheel encoders and IMU. For mapping, the quality of 2D occupancy grids is evaluated based on the geometry of the map and the representation of obstacles in the map to reduce the amount of manual postprocessing required. The sensor combination that created the best quality map is then changed to cheaper sensor subsets and compared among each other during localization to generate a guideline for sensor selection based on cost constraints and localization performance requirements. Then, the fused wheel encoder and IMU odometry is determine to be necessary and cannot be removed to further reduce cost after comparing their performance with exteroceptive odometry. Finally, the deployment of robotic wheelchair in a large environment is made more feasible due to the reduced cost using RTAB-Map with 3D LiDAR and two RGB-D cameras for mapping and RTAB-Map with 2D LiDAR and one RGB-D camera for localization with fused wheel encoder and IMU odometry for both cases.
author2 Ang Wei Tech
author_facet Ang Wei Tech
Lim, Jit Ern
format Final Year Project
author Lim, Jit Ern
author_sort Lim, Jit Ern
title Large scale localization and mapping for deployment of robotic wheelchair in a building
title_short Large scale localization and mapping for deployment of robotic wheelchair in a building
title_full Large scale localization and mapping for deployment of robotic wheelchair in a building
title_fullStr Large scale localization and mapping for deployment of robotic wheelchair in a building
title_full_unstemmed Large scale localization and mapping for deployment of robotic wheelchair in a building
title_sort large scale localization and mapping for deployment of robotic wheelchair in a building
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176826
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