Object detection using OTC LiDAR sensors
Autonomous navigation of unmanned aerial vehicles (UAVs) relies heavily on the capabilities of both onboard and attached visual sensors to provide data that can be processed for intelligent decision making during aerial operations. In this paper, we consider the problem of existing navigational t...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/157862 |
| _version_ | 1826118762767581184 |
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| author | Tan, Mark Jen Wei |
| author2 | Wen Bihan |
| author_facet | Wen Bihan Tan, Mark Jen Wei |
| author_sort | Tan, Mark Jen Wei |
| collection | NTU |
| description | Autonomous navigation of unmanned aerial vehicles (UAVs) relies heavily
on the capabilities of both onboard and attached visual sensors to provide data that
can be processed for intelligent decision making during aerial operations. In this
paper, we consider the problem of existing navigational tools such as RGB cameras
and Global Positioning Systems (GPS) to be limited in functionality for certain usecases within autonomous navigation.
Recent literature has suggested that LiDAR sensors show significant
potential in adding value to this field due to their ability to transmit their own signals
and create precise 3D coordinate data. However, most commercially in-use LiDAR
sensors are extremely expensive, making it economically unviable to conduct
extensive testing. In this work, we propose the use of low-cost off-the-counter (OTC)
LiDAR sensors to conduct object detection as a proof of concept for their use in
autonomous navigations use-cases. The usage of these sensors will enable us to
mitigate the financial constraints of extensive testing. We also propose the use of a
deep learning point cloud object detection model, PointPillars, as a complementing
method for our OTC LiDAR sensor due to the network’s ability to have a balance of
low computational requirements, fast speeds and high accuracy when compared to
similar 3D object detection networks.
For this project, we ran tests involving data collected from the L515 to detect
vehicular objects using a pre-trained PointPillars network. Extensive testingshows
that despite inaccuracies involving our deep learning model detecting objects from
data collected using the L515, our concept has been proven with moderate success.
We inferred based on our results that low-cost LiDAR sensors could add value to
indoor autonomous navigation, as well as use cases in environments without
significant ambient light and where range is not a demanding factor. Furthermore, a
pipeline, accompanying functions and a GUI for the L515 on MATLAB’s platform
has been shared to provide future researchers with the tools to conduct more tests in
this area. Finally, we have documented several key issues with respect to the L515,
as well as possible solutions that can be explored in future work. This information
will prove useful when extrapolated to other short range LiDAR sensors |
| first_indexed | 2024-10-01T04:48:47Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157862 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:48:47Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1578622023-07-07T19:03:02Z Object detection using OTC LiDAR sensors Tan, Mark Jen Wei Wen Bihan School of Electrical and Electronic Engineering Satellite Research Centre bihan.wen@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems Autonomous navigation of unmanned aerial vehicles (UAVs) relies heavily on the capabilities of both onboard and attached visual sensors to provide data that can be processed for intelligent decision making during aerial operations. In this paper, we consider the problem of existing navigational tools such as RGB cameras and Global Positioning Systems (GPS) to be limited in functionality for certain usecases within autonomous navigation. Recent literature has suggested that LiDAR sensors show significant potential in adding value to this field due to their ability to transmit their own signals and create precise 3D coordinate data. However, most commercially in-use LiDAR sensors are extremely expensive, making it economically unviable to conduct extensive testing. In this work, we propose the use of low-cost off-the-counter (OTC) LiDAR sensors to conduct object detection as a proof of concept for their use in autonomous navigations use-cases. The usage of these sensors will enable us to mitigate the financial constraints of extensive testing. We also propose the use of a deep learning point cloud object detection model, PointPillars, as a complementing method for our OTC LiDAR sensor due to the network’s ability to have a balance of low computational requirements, fast speeds and high accuracy when compared to similar 3D object detection networks. For this project, we ran tests involving data collected from the L515 to detect vehicular objects using a pre-trained PointPillars network. Extensive testingshows that despite inaccuracies involving our deep learning model detecting objects from data collected using the L515, our concept has been proven with moderate success. We inferred based on our results that low-cost LiDAR sensors could add value to indoor autonomous navigation, as well as use cases in environments without significant ambient light and where range is not a demanding factor. Furthermore, a pipeline, accompanying functions and a GUI for the L515 on MATLAB’s platform has been shared to provide future researchers with the tools to conduct more tests in this area. Finally, we have documented several key issues with respect to the L515, as well as possible solutions that can be explored in future work. This information will prove useful when extrapolated to other short range LiDAR sensors Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-24T02:38:04Z 2022-05-24T02:38:04Z 2022 Final Year Project (FYP) Tan, M. J. W. (2022). Object detection using OTC LiDAR sensors. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157862 https://hdl.handle.net/10356/157862 en A3284-211 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Computer hardware, software and systems Tan, Mark Jen Wei Object detection using OTC LiDAR sensors |
| title | Object detection using OTC LiDAR sensors |
| title_full | Object detection using OTC LiDAR sensors |
| title_fullStr | Object detection using OTC LiDAR sensors |
| title_full_unstemmed | Object detection using OTC LiDAR sensors |
| title_short | Object detection using OTC LiDAR sensors |
| title_sort | object detection using otc lidar sensors |
| topic | Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/157862 |
| work_keys_str_mv | AT tanmarkjenwei objectdetectionusingotclidarsensors |