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About this paper
Xiangdi Yue, Yihuan Zhang, Miaolei He
The review organizes LiDAR-based SLAM research by sensor configuration—2D, 3D, and spinning-actuated LiDAR—and connects these hardware choices to odometry, loop closure, and map optimization methods. It surveys representative systems and outlines persistent challenges, but does not introduce a new algorithm or provide a standardized performance comparison.
Key insights
- Sensor configuration determines the information available to a SLAM system: 2D scanners suit planar mapping, while 3D and mechanically rotated configurations provide broader spatial coverage with additional computational or calibration demands.
- No single architecture is established as best across applications; systems trade off accuracy, robustness, computational cost, and sensor coverage.
- Recurring research needs include robustness in geometrically degraded and dynamic environments, lifelong and large-scale mapping, multi-robot collaboration, sensor fusion, and semantic methods.
Summary by alphaXiv.
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