Visual Localization in a Prior 3D LiDAR Map Combining Points and Lines

Zhou, Guangzhao; Yuan, Haihui; Zhu, Shiqiang; Zhi-yong, Huang; Fan, Yanfu; Zhong, Xinliang; Du, Rong-Gui; Gu, Jiande

doi:10.1109/robio54168.2021.9739475

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…Experiments on the KITTI dataset produced consistently registered colored point clouds. G. Zhou et al [149] proposed a visual localization algorithm combining points and lines. Using the most advanced simultaneous localization and mapping algorithms at that time (such as LIO-SAM, LVI-SLAM, and Fast-LIO), 2D lines in the image could be extracted online, and 3D lines in the 3D LIDAR map could be extracted offline.…”

Section: Fusion Methods Based On Uncertaintymentioning

confidence: 99%

SLAM Overview: From Single Sensor to Heterogeneous Fusion

et al. 2022

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After decades of development, LIDAR and visual SLAM technology has relatively matured and been widely used in the military and civil fields. SLAM technology enables the mobile robot to have the abilities of autonomous positioning and mapping, which allows the robot to move in indoor and outdoor scenes where GPS signals are scarce. However, SLAM technology relying only on a single sensor has its limitations. For example, LIDAR SLAM is not suitable for scenes with highly dynamic or sparse features, and visual SLAM has poor robustness in low-texture or dark scenes. However, through the fusion of the two technologies, they have great potential to learn from each other. Therefore, this paper predicts that SLAM technology combining LIDAR and visual sensors, as well as various other sensors, will be the mainstream direction in the future. This paper reviews the development history of SLAM technology, deeply analyzes the hardware information of LIDAR and cameras, and presents some classical open source algorithms and datasets. According to the algorithm adopted by the fusion sensor, the traditional multi-sensor fusion methods based on uncertainty, features, and novel deep learning are introduced in detail. The excellent performance of the multi-sensor fusion method in complex scenes is summarized, and the future development of multi-sensor fusion method is prospected.

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Section: Fusion Methods Based On Uncertaintymentioning

confidence: 99%