CLINS: Continuous-Time Trajectory Estimation for LiDAR-Inertial System

Lv, Jiajun; Hu, Kangmin; Xu, Jinhong; Liu, Yong; Ma, Xikui; Zuo, Xingxing

doi:10.1109/iros51168.2021.9636676

Cited by 29 publications

(33 citation statements)

References 21 publications

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“…We refer to our method as continuous-time LIOM based on the use of continuous-time factor r L (L( Bt s f ), Xm , Xm+1 ), which is based on raw lidar point Bt s f . This characterization is consistent with previous works [7], [14]. In contrast, discrete-time methods use the deskewed point Bt m f based on IMU-propagated states [3], [4], [18].…”

Section: Maximum-a-priori (Map) Optimizationsupporting

confidence: 90%

“…In the first one, planar and corner features are extracted based on the smoothness value, then associated with k-nearest neighbours to construct the corresponding cost factors. This method was proposed in [2] and is still widely adopted in many recent works [3]- [7]. One issue of this method is that the smoothness calculation is specialized for the lidar scans with horizontally separated rings, in environments with man-made structure.…”

Section: Introductionmentioning

confidence: 99%

“…However, as the map grows, recomputing the k-d tree on the global map is intractable. One strategy to avoid this issue is to organize the maps into keyframes and build a local map with bounded size from a finite number of nearest keyframes [3]- [5], [7], [14]. However this strategy is sub-optimal since there are cases where the local map misses important prior observations that were captured in previous keyframes (an example is given in Fig.…”

Section: Introductionmentioning

confidence: 99%

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SLICT: Multi-input Multi-scale Surfel-Based Lidar-Inertial Continuous-Time Odometry and Mapping

Nguyen¹,

Duberg²,

Yuan³

et al. 2022

Preprint

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While feature association to a global map has significant benefits, to keep the computations from growing exponentially, most lidar-based odometry and mapping methods opt to associate features with local maps at one voxel scale. Taking advantage of the fact that surfels (surface elements) at different voxel scales can be organized in a tree-like structure, we propose an octree-based global map of multiscale surfels that can be updated incrementally. This alleviates the need for recalculating, for example, a k-d tree of the whole map repeatedly. The system can also take input from a single or a number of sensors, reinforcing the robustness in degenerate cases. We also propose a point-to-surfel (PTS) association scheme, continuous-time optimization on PTS and IMU preintegration factors, along with loop closure and bundle adjustment, making a complete framework for Lidar-Inertial continuous-time odometry and mapping. Experiments on public and in-house datasets demonstrate the advantages of our system compared to other state-of-the-art methods. To benefit the community, we release the source code and dataset at https: //github.com/brytsknguyen/slict.

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Section: Maximum-a-priori (Map) Optimizationsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SLICT: Multi-input Multi-scale Surfel-Based Lidar-Inertial Continuous-Time Odometry and Mapping

Nguyen¹,

Duberg²,

Yuan³

et al. 2022

Preprint

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“…By incorporating an inertial measurement unit (IMU), LiDAR inertial odometry (LIO) can present a better performance for localization and mapping in motion [ 13 ]. To make the implementation of LIO efficient and non-professional, self-calibration LIO methods have become a hot research topic in the related community [ 14 , 15 , 16 , 17 ]. Unlike the self-calibration odometer for common LiDAR that only considers the calibration between the IMU and the LIDAR, the self-calibration odometer for SLiDAR has to additionally consider the calibration between the rotating mechanism and the LIDAR due to the manufacturing and installation deviation, rotational wear and the nature of thermal expansion and contraction of the rotating mechanism [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

OMC-SLIO: Online Multiple Calibrations Spinning LiDAR Inertial Odometry

Wang

Zhang

Wang

2022

Sensors

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Light detection and ranging (LiDAR) is often combined with an inertial measurement unit (IMU) to get the LiDAR inertial odometry (LIO) for robot localization and mapping. In order to apply LIO efficiently and non-specialistically, self-calibration LIO is a hot research topic in the related community. Spinning LiDAR (SLiDAR), which uses an additional rotating mechanism to spin a common LiDAR and scan the surrounding environment, achieves a large field of view (FoV) with low cost. Unlike common LiDAR, in addition to the calibration between the IMU and the LiDAR, the self-calibration odometer for SLiDAR must also consider the mechanism calibration between the rotating mechanism and the LiDAR. However, existing self-calibration LIO methods require the LiDAR to be rigidly attached to the IMU and do not take the mechanism calibration into account, which cannot be applied to the SLiDAR. In this paper, we propose firstly a novel self-calibration odometry scheme for SLiDAR, named the online multiple calibration inertial odometer (OMC-SLIO) method, which allows online estimation of multiple extrinsic parameters among the LiDAR, rotating mechanism and IMU, as well as the odometer state. Specially, considering that the rotating and static parts of the motor encoder inside the SLiDAR are rigidly connected to the LiDAR and IMU respectively, we formulate the calibration within the SLiDAR as two separate sets of calibrations: the mechanism calibration between the LiDAR and the rotating part of the motor encoder and the sensor calibration between the static part of the motor encoder and the IMU. Based on such a SLiDAR calibration formulation, we can construct a well-defined kinematic model from the LiDAR to the IMU with the angular information from the motor encoder. Based on the kinematic model, a two-stage motion compensation method is presented to eliminate the point cloud distortion resulting from LiDAR spinning and platform motion. Furthermore, the mechanism and sensor calibration as well as the odometer state are wrapped in a measurement model and estimated via an error-state iterative extended Kalman filter (ESIEKF). Experimental results show that our OMC-SLIO is effective and attains excellent performance.

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“…T HE LiDAR-IMU (LI) system has prevailed in recent years [1][2][3][4][5][6][7], as an increasing number of robotic applications require accurate and robust LI-navigation solutions. Accurate LiDAR-IMU extrinsic calibration is a prerequisite of LI navigation.…”

Section: Introductionmentioning

confidence: 99%

Observability-Aware Intrinsic and Extrinsic Calibration of LiDAR-IMU Systems

Lv¹,

Zuo²,

Hu³

et al. 2022

Preprint

Self Cite

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Accurate and reliable sensor calibration is essential to fuse LiDAR and inertial measurements, which are usually available in robotic applications. In this paper, we propose a novel LiDAR-IMU calibration method within the continuoustime batch-optimization framework, where the intrinsics of both sensors and the spatial-temporal extrinsics between sensors are calibrated without using calibration infrastructure such as fiducial tags. Compared to discrete-time approaches, the continuoustime formulation has natural advantages for fusing high rate measurements from LiDAR and IMU sensors. To improve efficiency and address degenerate motions, two observabilityaware modules are leveraged: (i) The information-theoretic data selection policy selects only the most informative segments for calibration during data collection, which significantly improves the calibration efficiency by processing only the selected informative segments. (ii) The observability-aware state update mechanism in nonlinear least-squares optimization updates only the identifiable directions in the state space with truncated singular value decomposition (TSVD), which enables accurate calibration results even under degenerate cases where informative data segments are not available. The proposed LiDAR-IMU calibration approach has been validated extensively in both simulated and real-world experiments with different robot platforms, demonstrating its high accuracy and repeatability in commonly-seen human-made environments. We also open source our codebase to benefit the research community: https://github.com/APRIL-ZJU/OA-LICalib.

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CLINS: Continuous-Time Trajectory Estimation for LiDAR-Inertial System

Cited by 29 publications

References 21 publications

SLICT: Multi-input Multi-scale Surfel-Based Lidar-Inertial Continuous-Time Odometry and Mapping

SLICT: Multi-input Multi-scale Surfel-Based Lidar-Inertial Continuous-Time Odometry and Mapping

OMC-SLIO: Online Multiple Calibrations Spinning LiDAR Inertial Odometry

Observability-Aware Intrinsic and Extrinsic Calibration of LiDAR-IMU Systems

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