Point‐LIO: Robust High‐Bandwidth Light Detection and Ranging Inertial Odometry

He, Dongjiao; Wei, Xu; Chen, Nan; Kong, Fanze; Yuan, Chongjian; Zhang, Fu

doi:10.1002/aisy.202200459

Cited by 11 publications

(2 citation statements)

References 61 publications

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“…A direction to improve this work was to add additional features such as edge to the voxel to expand the universality of the proposed map representation method. Point-LIO (He et al, 2023) is a robust and high-bandwidth light detection and ranging (LiDAR) inertial odometry with the capability to estimate extremely aggressive robotic motions. Point-LIO could be used in various autonomous tasks, such as trajectory planning, control and perception, especially in cases involving very fast ego-motions.…”

Section: D Light Detection and Ranging-based Simultaneous Localizatio...mentioning

confidence: 99%

LiDAR-based SLAM for robotic mapping: state of the art and new frontiers

Yue,

Zhang,

Chen

et al. 2024

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Purpose In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping. Design/methodology/approach This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations. Findings This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future. Originality/value To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.

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Section: D Light Detection and Ranging-based Simultaneous Localizatio...mentioning

confidence: 99%

LiDAR-based SLAM for robotic mapping: state of the art and new frontiers

Yue,

Zhang,

Chen

et al. 2024

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show abstract

“…Keyframes are optimized both in a sliding window manner and after loop closure detections including IMU data. FAST-LIO2 [20] and Point-LIO [21] perform scan-to-map matching with the LiDAR point clouds and use Kalman filters for joint state estimation with the IMU data.…”

Section: B Lidar (Inertial) Odometrymentioning

confidence: 99%

LiDAR Ground Segmentation and Modeling for Mobile Robots in Unstructured Terrain

Skuddis

Koeberle

Haala

2022

2022 8th International Conference on Automation, Robotics and Applications (ICARA)

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We propose a new method for fine registering multiple point clouds simultaneously. The approach is characterized by being dense, therefore point clouds are not reduced to pre-selected features in advance. Furthermore, the approach is robust against small overlaps and dynamic objects, since no direct correspondences are assumed between point clouds. Instead, all points are merged into a global point cloud, whose scattering is then iteratively reduced. This is achieved by dividing the global point cloud into uniform grid cells whose contents are subsequently modeled by normal distributions. We show that the proposed approach can be used in a sliding window continuous trajectory optimization combined with IMU measurements to obtain a highly accurate and robust LiDAR inertial odometry estimation. Furthermore, we show that the proposed approach is also suitable for large scale keyframe optimization to increase accuracy. We provide the source code and some experimental data on https://github.com/davidskdds/DMSA_LiDAR_SLAM.git.

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LiDAR odometry survey: recent advancements and remaining challenges

Lee,

Jung,

Yang

et al. 2024

Intel Serv Robotics

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Odometry is crucial for robot navigation, particularly in situations where global positioning methods like global positioning system are unavailable. The main goal of odometry is to predict the robot’s motion and accurately determine its current location. Various sensors, such as wheel encoder, inertial measurement unit (IMU), camera, radar, and Light Detection and Ranging (LiDAR), are used for odometry in robotics. LiDAR, in particular, has gained attention for its ability to provide rich three-dimensional (3D) data and immunity to light variations. This survey aims to examine advancements in LiDAR odometry thoroughly. We start by exploring LiDAR technology and then scrutinize LiDAR odometry works, categorizing them based on their sensor integration approaches. These approaches include methods relying solely on LiDAR, those combining LiDAR with IMU, strategies involving multiple LiDARs, and methods fusing LiDAR with other sensor modalities. In conclusion, we address existing challenges and outline potential future directions in LiDAR odometry. Additionally, we analyze public datasets and evaluation methods for LiDAR odometry. To our knowledge, this survey is the first comprehensive exploration of LiDAR odometry.

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Point‐LIO: Robust High‐Bandwidth Light Detection and Ranging Inertial Odometry

Cited by 11 publications

References 61 publications

LiDAR-based SLAM for robotic mapping: state of the art and new frontiers

LiDAR-based SLAM for robotic mapping: state of the art and new frontiers

LiDAR Ground Segmentation and Modeling for Mobile Robots in Unstructured Terrain

LiDAR odometry survey: recent advancements and remaining challenges

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