Real-Time 3D Mapping in Complex Environments Using a Spinning Actuated LiDAR System

Li, Yan; Dai, Junhu; Zhao, Yinghao; Chen, Changjun

doi:10.3390/rs15040963

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…Wang et al (2022a) presented the online multiple calibration inertial odometer (OMC-SLIO) approach for SLiDAR (spinning LiDAR), which estimated numerous extrinsic parameters of the LiDAR, rotating mechanism, IMU and odometer state online. Yan et al (2023) introduced Spin-LOAM, a tightly coupled 3D LiDAR-based SLAM algorithm for spinning-actuated LiDAR systems. There was an adaptive scan accumulation method that analysed feature point spatial distribution to increase matching accuracy and reliability.…”

Section: Light Detection and Ranging-based Simultaneous Localization ...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: Light Detection and Ranging-based Simultaneous Localization ...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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“…Odometry and mapping, also known as simultaneous localization and mapping (SLAM) [1], is an important task for autonomous mobile systems to localize themselves and interact with surroundings in unknown environments. RGB cameras and light detection and ranging (LiDAR) are two commonly used sensors for SLAM.…”

Section: Introductionmentioning

confidence: 99%

RSS-LIWOM: Rotating Solid-State LiDAR for Robust LiDAR-Inertial-Wheel Odometry and Mapping

et al. 2023

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Solid-state LiDAR offers multiple advantages over mechanism mechanical LiDAR, including higher durability, improved coverage ratio, and lower prices. However, solid-state LiDARs typically possess a narrow field of view, making them less suitable for odometry and mapping systems, especially for mobile autonomous systems. To address this issue, we propose a novel rotating solid-state LiDAR system that incorporates a servo motor to continuously rotate the solid-state LiDAR, expanding the horizontal field of view to 360∘. Additionally, we propose a multi-sensor fusion odometry and mapping algorithm for our developed sensory system that integrates an IMU, wheel encoder, motor encoder and the LiDAR into an iterated Kalman filter to obtain a robust odometry estimation. Through comprehensive experiments, we demonstrate the effectiveness of our proposed approach in both outdoor open environments and narrow indoor environments.

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“…Inertial stabilization platforms (ISPs) play an increasingly important role in the field of remote sensing, such as optical imaging equipment, tracking radar, antenna, and so on [1][2][3][4][5][6], to isolate the influence of carrier motion on pointing accuracy. With the increase in load weight range (10 kg~100 kg), ISPs need a precision reducer to enlarge the driving torque and ensure high transmission accuracy.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Parameter Estimation of Inertial Stabilization Platform with Unknown Load

Zheng

Xie

et al. 2023

Actuators

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In order to improve the state monitoring and adaptive control capability of inertial stabilization platforms (ISPs) with unknown loads, it is necessary to estimate the dynamic parameters comprehensively online. However, most current online estimation methods regard the system as a linear dual-inertia model which neglects the backlash and nonlinear friction torque. It reduces the accuracy of the model and leads to incomplete and low accuracy of the estimated parameters. The purpose of this research is to achieve a comprehensive and accurate online estimation of multiple parameters of ISPs and lay a foundation for state monitoring and adaptive control of ISPs. First, a dual-inertia model containing backlash and nonlinear friction torque of the motor and load is established. Then, the auto-regressive moving average (ARMA) model of the motor and load is established by the forward Euler method, which clearly expresses the online identification formula of the parameters. On this basis, the adaptive identification method based on the recursive extended least squares (RELS) algorithm is used to realize the online estimation of multiple parameters. The simulation and experimental results show that the proposed adaptive multi-parameter estimation method can realize the simultaneous online identification of the moment of inertia of the load, the damping coefficient of motor and load, the transmission stiffness, the Coulomb friction torque of motor and load, and the backlash, and the steady-state error is less than 10%. Compared with the traditional linear dual-inertia model, the similarity between the model based on the proposed adaptive parameter estimation algorithm and the actual system is increased by 65.3%.

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Real-Time 3D Mapping in Complex Environments Using a Spinning Actuated LiDAR System

Cited by 7 publications

References 31 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

RSS-LIWOM: Rotating Solid-State LiDAR for Robust LiDAR-Inertial-Wheel Odometry and Mapping

Adaptive Multi-Parameter Estimation of Inertial Stabilization Platform with Unknown Load

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