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 34 publications

(8 citation statements)

References 61 publications

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“…Additionally, Zhao et al introduce the iterated ESKFS to mitigate potential degeneration issues caused by increased sub-frames. Point-LIO [47] achieves high-frequency odometry through a point-by-point framework. This approach involves processing LiDAR scans at the individual point level, a strategy that naturally eliminates motion distortion.…”

Section: Tightly Coupled Approachesmentioning

confidence: 99%

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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Section: Tightly Coupled Approachesmentioning

confidence: 99%

LiDAR odometry survey: recent advancements and remaining challenges

Lee,

Jung,

Yang

et al. 2024

Intel Serv Robotics

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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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“…The implementation considers four different factors, namely IMU preintegration, LiDAR odometry, GPS and a loop closure factor, making it ideal for multi-sensor fusion and global optimization. Lately, several methods based on similar principles have been proposed [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ].…”

Section: Background and Related Workmentioning

confidence: 99%

A LiDAR-Camera-Inertial-GNSS Apparatus for 3D Multimodal Dataset Collection in Woodland Scenarios

Cristóvão,

Portugal,

Carvalho

et al. 2023

Sensors

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Forestry operations have become of great importance for a sustainable environment in the past few decades due to the increasing toll induced by rural abandonment and climate change. Robotics presents a promising solution to this problem; however, gathering the necessary data for developing and testing algorithms can be challenging. This work proposes a portable multi-sensor apparatus to collect relevant data generated by several onboard sensors. The system incorporates Laser Imaging, Detection and Ranging (LiDAR), two stereo depth cameras and a dedicated inertial measurement unit (IMU) to obtain environmental data, which are coupled with an Android app that extracts Global Navigation Satellite System (GNSS) information from a cell phone. Acquired data can then be used for a myriad of perception-based applications, such as localization and mapping, flammable material identification, traversability analysis, path planning and/or semantic segmentation toward (semi-)automated forestry actuation. The modular architecture proposed is built on Robot Operating System (ROS) and Docker to facilitate data collection and the upgradability of the system. We validate the apparatus’ effectiveness in collecting datasets and its flexibility by carrying out a case study for Simultaneous Localization and Mapping (SLAM) in a challenging woodland environment, thus allowing us to compare fundamentally different methods with the multimodal system proposed.

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

Cited by 34 publications

References 61 publications

LiDAR odometry survey: recent advancements and remaining challenges

LiDAR odometry survey: recent advancements and remaining challenges

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

A LiDAR-Camera-Inertial-GNSS Apparatus for 3D Multimodal Dataset Collection in Woodland Scenarios

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