Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels

Bai, Chunge; Xiao, Tao; Chen, Yajie; Wang, Haoqian; Zhang, Fang; Gao, Xiang

doi:10.1109/lra.2022.3152830

Cited by 144 publications

(49 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A robocentric formulation is proposed to average the noisy measurements of different sensors; then, the degeneracy and divergency can be avoided. There is also a series of advanced lidar-inertial odometry [ 19 , 20 , 21 , 22 ] developed from the methodology of LINS. These methods show the advantage of the accuracy of pose estimation, but, as the idea of an averaging error is not suitable for severe degeneration, failure solutions may occur and the direction of degeneration cannot be determined in these methods.…”

Section: Related Workmentioning

confidence: 99%

Robust Lidar-Inertial Odometry with Ground Condition Perception and Optimization Algorithm for UGV

Zhao

Zhang

Shi

et al. 2022

Sensors

View full text Add to dashboard Cite

Unmanned ground vehicles (UGVs) are making more and more progress in many application scenarios in recent years, such as exploring unknown wild terrain, working in precision agriculture and serving in emergency rescue. Due to the complex ground conditions and changeable surroundings of these unstructured environments, it is challenging for these UGVs to obtain robust and accurate state estimations by using sensor fusion odometry without prior perception and optimization for specific scenarios. In this paper, based on an error-state Kalman filter (ESKF) fusion model, we propose a robust lidar-inertial odometry with a novel ground condition perception and optimization algorithm specifically designed for UGVs. The probability distribution gained from the raw inertial measurement unit (IMU) measurements during a certain time period and the state estimation of ESKF were both utilized to evaluate the flatness of ground conditions in real-time; then, by analyzing the relationship between the current ground condition and the accuracy of the state estimation, the tightly coupled lidar-inertial odometry was dynamically optimized further by adjusting the related parameters of the processing algorithm of the lidar points to obtain robust and accurate ego-motion state estimations of UGVs. The method was validated in various types of environments with changeable ground conditions, and the robustness and accuracy are shown through the consistent accurate state estimation in different ground conditions compared with the state-of-art lidar-inertial odometry systems.

show abstract

Section: Related Workmentioning

confidence: 99%

Robust Lidar-Inertial Odometry with Ground Condition Perception and Optimization Algorithm for UGV

Zhao

Zhang

Shi

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The original system has been improved in two aspects of accuracy and real-time. In order to improve the tracking speed of the Fast-Lio2 algorithm, Bai et al proposed the Faster-Lio algorithm [13], which improved the traditional voxel and proposed the point cloud spatial data structure of the incremental voxel without using the two main data structures of strict K-nearest neighbour and complex incremental KD tree, thus improving the operation speed of the system. Liu et al used a multi-state constraint Kalman filter to fuse visual observation and IMU pre-integration information and used Helmert variance component estimation to adjust the weight of visual features and IMU pre-integration, so as to realize the accurate estimation of camera pose [14].…”

Section: Relate Workmentioning

confidence: 99%

Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry

Chen

Zhao

Zhu

et al. 2022

Sensors

View full text Add to dashboard Cite

In this paper, we propose a visual marker-aided LiDAR/IMU/encoder integrated odometry, Marked-LIEO, to achieve pose estimation of mobile robots in an indoor long corridor environment. In the first stage, we design the pre-integration model of encoder and IMU respectively to realize the pose estimation combined with the pose estimation from the second stage providing prediction for the LiDAR odometry. In the second stage, we design low-frequency visual marker odometry, which is optimized jointly with LiDAR odometry to obtain the final pose estimation. In view of the wheel slipping and LiDAR degradation problems, we design an algorithm that can make the optimization weight of encoder odometry and LiDAR odometry adjust adaptively according to yaw angle and LiDAR degradation distance respectively. Finally, we realize the multi-sensor fusion localization through joint optimization of an encoder, IMU, LiDAR, and camera measurement information. Aiming at the problems of GNSS information loss and LiDAR degradation in indoor corridor environment, this method introduces the state prediction information of encoder and IMU and the absolute observation information of visual marker to achieve the accurate pose of indoor corridor environment, which has been verified by experiments in Gazebo simulation environment and real environment.

show abstract

“…Furthermore, instead of point-wise operations the authors of Faster-LIO [16] proposed voxel-wise operations for point cloud association across frames and reported improved efficiency. In our work we also maintain an ikd-tree of the fused lidar measurements and tightly couple the relative lidar poses, IMU preintegration and GNSS prior in our propose estimator.…”

Section: A Direct Tightly Coupled Multi-lidar Odometrymentioning

confidence: 99%

M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance

Das¹,

Mahabadi²,

Fallon³

et al. 2022

Preprint

View full text Add to dashboard Cite

We present a robust system for state estimation that fuses measurements from multiple lidars and inertial sensors with GNSS data. To initiate the method, we use the prior GNSS pose information. We then perform incremental motion in real-time, which produces robust motion estimates in a global frame by fusing lidar and IMU signals with GNSS translation components using a factor graph framework. We also propose methods to account for signal loss with a novel synchronization and fusion mechanism. To validate our approach extensive tests were carried out on data collected using Scania test vehicles (5 sequences for a total of ≈ 7 Km). From our evaluations, we show an average improvement of 61% in relative translation and 42% rotational error compared to a state-of-the-art estimator fusing a single lidar/inertial sensor pair.

show abstract

Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels

Cited by 144 publications

References 48 publications

Robust Lidar-Inertial Odometry with Ground Condition Perception and Optimization Algorithm for UGV

Robust Lidar-Inertial Odometry with Ground Condition Perception and Optimization Algorithm for UGV

Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry

M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance

Contact Info

Product

Resources

About