Graph SLAM-Based 2.5D LIDAR Mapping Module for Autonomous Vehicles
Abstract
:1. Introduction
2. Key Solution and Proposed Strategy
2.1. Node Domain
2.2. GS Optimization Strategy in Node Domain
3. The Proposed Graph SLAM Framework (GS-XYZ)
3.1. Edge Selection and Calculation
3.2. Cost Function Concept (Example: GS-XY)
3.3. Transforming GS-XY to GS-Z
4. Experimental Platform and Test Course
4.1. Platform Configuration and Framework Setups
4.2. Test Course
5. Results and Discussion
5.1. Graph SLAM in the XY Plane
5.2. Graph SLAM in the Z Plane
6. Conclusions
7. Patents
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviation
GS | Graph SLAM |
DR | Dead Reckoning |
GIR | GNSS/INS-RTK |
PhC | Phase Correlation |
ICP | Iterative Closest Point |
ACS | Absolute Coordinate System |
LIDAR | Light Detection and Ranging |
SLAM | Simultaneous Localization and Mapping |
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Aldibaja, M.; Suganuma, N. Graph SLAM-Based 2.5D LIDAR Mapping Module for Autonomous Vehicles. Remote Sens. 2021, 13, 5066. https://doi.org/10.3390/rs13245066
Aldibaja M, Suganuma N. Graph SLAM-Based 2.5D LIDAR Mapping Module for Autonomous Vehicles. Remote Sensing. 2021; 13(24):5066. https://doi.org/10.3390/rs13245066
Chicago/Turabian StyleAldibaja, Mohammad, and Naoki Suganuma. 2021. "Graph SLAM-Based 2.5D LIDAR Mapping Module for Autonomous Vehicles" Remote Sensing 13, no. 24: 5066. https://doi.org/10.3390/rs13245066
APA StyleAldibaja, M., & Suganuma, N. (2021). Graph SLAM-Based 2.5D LIDAR Mapping Module for Autonomous Vehicles. Remote Sensing, 13(24), 5066. https://doi.org/10.3390/rs13245066