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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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 |
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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 |
_version_ |
1814047222506979328 |