LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation

Qin, Chao; Ye, Haoyang; Pranata, Christian E.; Han, Jun; Zhang, Shuyang; Liu, Ming

doi:10.1109/icra40945.2020.9197567

Cited by 221 publications

(147 citation statements)

References 29 publications

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“…Although the accuracy of loosely coupled EKF fusion is not particularly high, it is simple in construction and can be implemented quickly. The representative method of tightly coupled EKF fusion is robocentric LiDAR-inertial state estimator (R-LINS) [ 93 ], which uses the error state Kalman filter (ESKF) model to minimize nonlinear constraints, thereby achieving the update of the agent’s posture. It provides comparable performance to the most advanced LiDAR-inertial odometry in terms of stability and accuracy, and an order of magnitude improvement in speed.…”

Section: Multi-sensors Fusion Navigation Methodsmentioning

confidence: 99%

“…Table 2 presents a summary of multi-sensor fusion methods for agent navigation. Fusion loop detection [86,87] Optimization of 3D LiDAR point cloud [13] Based on deep learning [88,89] Based on DRL [90,91] IMU Feature-level fusion (based on EKF [92,93], nonlinear optimization method [35,94,95] Decision-level fusion (Fusion motion estimation [31,97])…”

Section: Othersmentioning

confidence: 99%

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An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

Yang

Zhang

et al. 2021

Sensors

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Indoor autonomous navigation refers to the perception and exploration abilities of mobile agents in unknown indoor environments with the help of various sensors. It is the basic and one of the most important functions of mobile agents. In spite of the high performance of the single-sensor navigation method, multi-sensor fusion methods still potentially improve the perception and navigation abilities of mobile agents. This work summarizes the multi-sensor fusion methods for mobile agents’ navigation by: (1) analyzing and comparing the advantages and disadvantages of a single sensor in the task of navigation; (2) introducing the mainstream technologies of multi-sensor fusion methods, including various combinations of sensors and several widely recognized multi-modal sensor datasets. Finally, we discuss the possible technique trends of multi-sensor fusion methods, especially its technique challenges in practical navigation environments.

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Section: Multi-sensors Fusion Navigation Methodsmentioning

confidence: 99%

Section: Othersmentioning

confidence: 99%

An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

Yang

Zhang

et al. 2021

Sensors

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show abstract

“…

), the Kalman gain equation

, and the new error state

were calculated according to the Kalman filtering (KF) principle.

When iterative matching was completed, the final pose

could be obtained from

, and the covariance matrix P K+1 required for state estimation at moment k + 1 could be obtained by Equation (11):

Synthesize the state in the global coordinate system [ 16 ] and proceed to the next step: optimization iteration after initialization. …”

Section: Methodsmentioning

confidence: 99%

“…Synthesize the state in the global coordinate system [ 16 ] and proceed to the next step: optimization iteration after initialization.…”

Section: Methodsmentioning

confidence: 99%

“…The application of this technology enables better results for tree crown measurements.The Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping) algorithm [ 14 ] uses the Visual-Inertial State Estimator (VINS-mono) monocular odometry’s dynamic initialization scheme [ 15 ], which relies on variable motion with roll angles, but large agricultural tracked robots cannot perform movement in rolls and pitches in a garden or orchard. The Robocentric lidar_inertial state Estimator (R-LINS) algorithm [ 16 ] uses a static solution to complete the system initialization and uses the method based on iterative error state Kalman filter (ESKF) to perform state estimation.…”

Section: Introductionmentioning

confidence: 99%

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Mobile LiDAR Scanning System Combined with Canopy Morphology Extracting Methods for Tree Crown Parameters Evaluation in Orchards

Wang

Zhou

Zhang

et al. 2021

Sensors

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To meet the demand for canopy morphological parameter measurements in orchards, a mobile scanning system is designed based on the 3D Simultaneous Localization and Mapping (SLAM) algorithm. The system uses a lightweight LiDAR-Inertial Measurement Unit (LiDAR-IMU) state estimator and a rotation-constrained optimization algorithm to reconstruct a point cloud map of the orchard. Then, Statistical Outlier Removal (SOR) filtering and European clustering algorithms are used to segment the orchard point cloud from which the ground information has been separated, and the k-nearest neighbour (KNN) search algorithm is used to restore the filtered point cloud. Finally, the height of the fruit trees and the volume of the canopy are obtained by the point cloud statistical method and the 3D alpha-shape algorithm. To verify the algorithm, tracked robots equipped with LIDAR and an IMU are used in a standardized orchard. Experiments show that the system in this paper can reconstruct the orchard point cloud environment with high accuracy and can obtain the point cloud information of all fruit trees in the orchard environment. The accuracy of point cloud-based segmentation of fruit trees in the orchard is 95.4%. The R2 and Root Mean Square Error (RMSE) values of crown height are 0.93682 and 0.04337, respectively, and the corresponding values of canopy volume are 0.8406 and 1.5738, respectively. In summary, this system achieves a good evaluation result of orchard crown information and has important application value in the intelligent measurement of fruit trees.

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

Wei

Chen

et al. 2023

Advanced Intelligent Systems

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Herein, point light detection and ranging inertial odometry (LIO) is presented: a robust and high‐bandwidth light detection and ranging (LiDAR) inertial odometry with the capability to estimate extremely aggressive robotic motions. Point‐LIO has two key novelties. The first one is a point‐by‐point LIO framework that updates the state at each LiDAR point measurement. This framework allows an extremely high‐frequency odometry output, significantly increases the odometry bandwidth, and fundamentally removes the artificial in‐frame motion distortion. The second one is a stochastic process‐augmented kinematic model which models the IMU measurement as an output. This new modeling method enables accurate localization and reliable mapping for aggressive motions even with inertial measurement unit (IMU) measurements saturated in the middle of the motion. Various real‐world experiments are conducted for performance evaluation. Overall, Point‐LIO is capable to provide accurate, high‐frequency odometry (4–8 kHz) and reliable mapping under severe vibrations and aggressive motions with high angular velocity (75 rad s−1) beyond the IMU measuring ranges. Furthermore, an exhaustive benchmark comparison is conducted. Point‐LIO achieves consistently comparable accuracy and time consumption. Finally, two example applications of Point‐LIO are demonstrated, one is a racing drone and the other is a self‐rotating unmanned aerial vehicle, both have aggressive motions.

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LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation

Cited by 221 publications

References 29 publications

An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

Mobile LiDAR Scanning System Combined with Canopy Morphology Extracting Methods for Tree Crown Parameters Evaluation in Orchards

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

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