Real-Time, Environmentally-Robust 3D LiDAR Localization

Zhu, Yilong; Xue, Bohuan; Zheng, Linwei; Huang, Huaiyang; Liu, Ming; Fan, Rui

doi:10.1109/ist48021.2019.9010305

Cited by 15 publications

(5 citation statements)

References 22 publications

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“…However, one of the key drawbacks of LiDAR is the high cost associated with acquiring and maintaining these high-resolution systems. Additionally, LiDAR can generate vast amounts of data, requiring robust computational resources for real-time processing and map building (Zhu et al, 2019). This becomes even more complex when LiDAR data is used in conjunction with other sensor modalities for multi-sensor fusion, which significantly increases the computational load.…”

Section: General Advantages and Limitations Of Lidarmentioning

confidence: 99%

Intelligent Systems in Motion

Cai,

Qin,

et al. 2023

International Journal on Semantic Web and Information Systems

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Simultaneous localization and mapping (SLAM) serves as a cornerstone in autonomous systems and has seen exponential growth in its roles, particularly in facilitating advanced path planning solutions. One emerging avenue of research that is rapidly evolving is the incorporation of multi-sensor fusion techniques to enhance SLAM-based path planning. The paper initiates with a thorough review of various sensor types and their attributes before covering a broad spectrum of both traditional and contemporary algorithms for multi-sensor fusion within SLAM. Performance evaluation metrics pertinent to SLAM and sensor fusion are explored. A special focus is laid on the interconnected roles and applications of multi-sensor fusion in SLAM-based path planning, discussing its significance in navigation scenarios as well as addressing challenges such as computational burden and real-time implementation. This paper sets the stage for future developments in creating more robust, resilient, and efficient SLAM-based path planning systems enabled by multi-sensor fusion.

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Section: General Advantages and Limitations Of Lidarmentioning

confidence: 99%

Intelligent Systems in Motion

Cai,

Qin,

et al. 2023

International Journal on Semantic Web and Information Systems

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“…We drove the vehicle through urban roads at an average speed of 3m/s. Ground-truth poses are obtained from a coupled LiDAR-GPS-encoder localization system that was proposed in [79], [80]. Table II shows the parameters which are empirically set in the system.…”

Section: A Implementation Detailsmentioning

confidence: 99%

Robust Odometry and Mapping for Multi-LiDAR Systems With Online Extrinsic Calibration

Jiao

Zhu

et al. 2022

IEEE Trans. Robot.

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Combining multiple LiDARs enables a robot to maximize its perceptual awareness of environments and obtain sufficient measurements, which is promising for simultaneous localization and mapping (SLAM). This paper proposes a system to achieve robust and simultaneous extrinsic calibration, odometry, and mapping for a multiple LiDARs. Our approach starts with measurement preprocessing to extract edge and planar features from raw measurements. After a motion and extrinsic initialization procedure, a sliding window-based multi-LiDAR odometry runs onboard to estimate poses with online calibration refinement and convergence identification. We further develop a mapping algorithm to construct a global map and optimize poses with sufficient features together with a method to model and reduce data uncertainty. We validate our approach's performance with extensive experiments on ten sequences (4.60km total length) for the calibration and SLAM and compare them against the state-of-the-art. We demonstrate that the proposed work is a complete, robust, and extensible system for various multi-LiDAR setups. The source code, datasets, and demonstrations are available at: https://ram-lab.com/file/site/m-loam.

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“…In an Expectation-Maximum (EM) scheme, they find the correspondences with the pose estimation and optimize the pose parameters with the association result. As an extension to frame-to-frame method, the frame-to-model pipeline is widely applied in SLAM [5], [16], [17] and localization [18], [19] systems for robotic state estimation. For example, in LOAM [5], scans in the past frames are aggregated into a global map with their estimated poses.…”

Section: A Pairwise Registrationmentioning

confidence: 99%