Degenerate Motion Analysis for Aided INS With Online Spatial and Temporal Sensor Calibration

Yang, Yulin; Geneva, Patrick; Eckenhoff, Kevin; Huang, Guoquan

doi:10.1109/lra.2019.2893803

Cited by 71 publications

(40 citation statements)

References 19 publications

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“…With IN2LAAMA's front-end being built upon planar and edge features, the trajectory needs to allow for the frame-to-frame registration of at least three non-coplanar planes or non-collinear edges to constrain the lidar pose estimation properly. As demonstrated in [32], the calibration parameters are not observable for every trajectory. The ideal ones are random paths that stimulate the IMU's six DoFs.…”

Section: F Real-data -Calibrationmentioning

confidence: 99%

IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping

2021

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In this paper, we present INertial Lidar Localisation Autocalibration And MApping (IN2LAAMA): an offline probabilistic framework for localisation, mapping, and extrinsic calibration based on a 3D-lidar and a 6-DoF-IMU. Most of today's lidars collect geometric information about the surrounding environment by sweeping lasers across their field of view. Consequently, 3D-points in one lidar scan are acquired at different timestamps. If the sensor trajectory is not accurately known, the scans are affected by the phenomenon known as motion distortion. The proposed method leverages preintegration with a continuous representation of the inertial measurements to characterise the system's motion at any point in time. It enables precise correction of the motion distortion without relying on any explicit motion model. The system's pose, velocity, biases, and time-shift are estimated via a full batch optimisation that includes automatically generated loop-closure constraints. The autocalibration and the registration of lidar data rely on planar and edge features matched across pairs of scans. The performance of the framework is validated through simulated and real-data experiments.

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Section: F Real-data -Calibrationmentioning

confidence: 99%

IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping

2021

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“…Zuo et al [31] developed a Lidar-inertial-camera odometry that could refine the spatial-temporal parameters online along with sensor pose estimation. Yang et al [32] analyzed the observability of spatial-temporal parameters and showed that they were both observable if the sensor platform underwent fully random motion. The authors also identified four degenerate motions that were harmful to the calibration accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…The online methods assumed that the measurements from a camera and an IMU were well synchronized (e.g., [14][15][16][17][18][19]), or the extrinsic spatial parameter was known in advance (e.g., [20][21][22]), or both conditions were satisfied (e.g., [23][24][25][26][27][28]). In the case where both the measurements from different sensors are asynchronous and the extrinsic spatial parameter between different sensors is unknown, most of the existing methods [29][30][31][32][33][34] are designed for filter-based VIOs since they are usually built on the Multi-State Constraint Kalman Filter (MSCKF [35]) framework. They perform the state propagation/prediction by integrating IMU measurements and perform the state update/correction by using visual measurements.…”

Section: Introductionmentioning

confidence: 99%

Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints

Huang

Wan

Liu

2021

Sensors

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The online system state initialization and simultaneous spatial-temporal calibration are critical for monocular Visual-Inertial Odometry (VIO) since these parameters are either not well provided or even unknown. Although impressive performance has been achieved, most of the existing methods are designed for filter-based VIOs. For the optimization-based VIOs, there is not much online spatial-temporal calibration method in the literature. In this paper, we propose an optimization-based online initialization and spatial-temporal calibration method for VIO. The method does not need any prior knowledge about spatial and temporal configurations. It estimates the initial states of metric-scale, velocity, gravity, Inertial Measurement Unit (IMU) biases, and calibrates the coordinate transformation and time offsets between the camera and IMU sensors. The work routine of the method is as follows. First, it uses a time offset model and two short-term motion interpolation algorithms to align and interpolate the camera and IMU measurement data. Then, the aligned and interpolated results are sent to an incremental estimator to estimate the initial states and the spatial–temporal parameters. After that, a bundle adjustment is additionally included to improve the accuracy of the estimated results. Experiments using both synthetic and public datasets are performed to examine the performance of the proposed method. The results show that both the initial states and the spatial-temporal parameters can be well estimated. The method outperforms other contemporary methods used for comparison.

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“…With IN2LAAMA's front-end being built upon planar features the trajectory of the lidar need to allow the frame-to-frame registration of minimum 3 non-coplanar planes to properly constrain the lidar pose estimation. As demonstrated in [33], some trajectory types do not lead to observable calibration parameters. The ideal trajectories are random paths that stimulate the 6-DoF of the IMU.…”

Section: E Real-data -Calibrationmentioning

confidence: 99%

IN2LAMA: INertial Lidar Localisation And MApping

Gentil

Vidal-Calleja

Huang

2019

2019 International Conference on Robotics and Automation (ICRA)

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In this paper, we present INertial Lidar Localisation Autocalibration And MApping (IN2LAAMA): a probabilistic framework for localisation, mapping, and extrinsic calibration based on a 3D-lidar and a 6-DoF-IMU. Most of today's lidars collect geometric information about the surrounding environment by sweeping lasers across their field of view. Consequently, 3Dpoints in one lidar scan are acquired at different timestamps. If the sensor trajectory is not accurately known, the scans are affected by the phenomenon known as motion distortion. The proposed method leverages preintegration with a continuous representation of the inertial measurements to characterise the system's motion at any point in time. It enables precise correction of the motion distortion without relying on any explicit motion model. The system's pose, velocity, biases, and time-shift are estimated via a full batch optimisation that includes automatically generated loop-closure constraints. The autcalibration and the registration of lidar data relies on planar and edge features matched across pairs of scans. The performance of the framework is validated through simulated and real-data experiments.

show abstract

Degenerate Motion Analysis for Aided INS With Online Spatial and Temporal Sensor Calibration

Cited by 71 publications

References 19 publications

IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping

IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping

Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints

IN2LAMA: INertial Lidar Localisation And MApping

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