IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping

Gentil, Cedric Le; Vidal-Calleja, Teresa; Huang, Shoudong

doi:10.1109/tro.2020.3018641

Cited by 50 publications

(48 citation statements)

References 32 publications

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“…Unfortunately, (1) does not have any known general analytical solution [4]. While an analytical method for the 1-axis-rotation scenario was proposed, they still rely on the iterative numerical integration of (5) over upsampled gyroscope readings to solve for the general case (as done in [14]). Overall, in real-world situations, the solution for the 1-axis-rotation is not practical due to the presence of biases in the gyroscope data breaking the property of rotation commutativity needed to analytically integrate the angular velocities.…”

Section: B Preintegrationmentioning

confidence: 99%

“…In simple words, the goal of our proposed GP integration over SO( 3) is to learn/optimise the values ofṙ ti t1 according to (14) and the given gyroscope readingsω…”

Section: So(3) Gaussian Process Integrationmentioning

confidence: 99%

“…The second objective of this experiment is to show the applicability of the UGPMs for inertial-aided multi-modal state estimation. For that purpose, we have integrated the UGPMs into a lidar-inertial localisation and mapping framework [14]. This framework uses the UGPMs to asynchronously characterise the system's motion while moving in the environment.…”

Section: Real-world Validationmentioning

confidence: 99%

“…The authors of [8,9] extended the preintegration theory from [15] to perform computations on the SO(3) manifold instead of the original Euler angle representation. Overall, preintegration allows for the pre-processing of the IMU into pseudomeasurements, greatly improving the efficiency of inertialaided multi-sensor localisation algorithms [11,14,16,17].…”

Section: Introductionmentioning

confidence: 99%

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Continuous Integration over SO(3) for IMU Preintegration

Gentil¹,

Vidal-Calleja²

2021

Robotics: Science and Systems XVII

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This paper presents a novel method for continuous integration over the 3D rotation group SO(3). The key idea is to model the system's dynamics with Gaussian Processes (GPs) and learn the GP training data to match the system's instantaneous angular velocity measurements. This is formulated as the minimisation of a simple cost function that leverages the application of linear operators over GP kernels. The proposed integration method over SO( 3) is applied to the preintegration of inertial data provided by a 6-DoF-Inertial Measurement Unit (IMU). Unlike standard integration that requires the recomputation of the integrals every time the estimate changes, preintegration combines the IMU readings into pseudo-measurements that are independent from the pose estimate and allows for efficient multimodal sensor-fusion. The pseudo-measurements generated by the proposed method are named Unified Gaussian Preintegrated Measurements (UGPMs). UGPMs rely on GP regression and linear operators to analytically integrate the acceleration data. Moreover, a mechanism for IMU bias and time-shift correction is introduced to allow for seamless multi-modal state estimation. Over quantitative experiments, we show that the UGPMs outperform the current state-of-the-art preintegration methods.

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Section: B Preintegrationmentioning

confidence: 99%

“…In simple words, the goal of our proposed GP integration over SO( 3) is to learn/optimise the values ofṙ ti t1 according to (14) and the given gyroscope readingsω…”

Section: So(3) Gaussian Process Integrationmentioning

confidence: 99%

Section: Real-world Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Continuous Integration over SO(3) for IMU Preintegration

Gentil¹,

Vidal-Calleja²

2021

Robotics: Science and Systems XVII

Self Cite

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“…Thus, there has been a growing focus on tightly-coupled LiDAR-inertial odometry, where point cloud and IMU measurements are fused in a joint optimization or filtering framework. Pre-integrated IMU readings are often employed for de-skewing the LiDAR scan per frame [14]. In [15], an optimization-based approach was proposed for LiDAR-inertial odometry using a maximum a posteriori (MAP) formulation incorporating both LiDAR and IMU residuals in a sliding window fashion.…”

Section: Introductionmentioning

confidence: 99%

Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping

Hanebeck

2021

IEEE Robot. Autom. Lett.

158

100

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We present a novel tightly-coupled LiDAR-inertial odometry and mapping scheme for both solid-state and mechanical LiDARs. As frontend, a feature-based lightweight LiDAR odometry provides fast motion estimates for adaptive keyframe selection. As backend, a hierarchical keyframe-based sliding window optimization is performed through marginalization for directly fusing IMU and LiDAR measurements. For the Livox Horizon, a newly released solid-state LiDAR, a novel feature extraction method is proposed to handle its irregular scan pattern during preprocessing. LiLi-OM (Livox LiDAR-inertial odometry and mapping) is real-time capable and achieves superior accuracy over state-of-the-art systems for both LiDAR types on public data sets of mechanical LiDARs and in experiments using the Livox Horizon. Source code and recorded experimental data sets are available at https://github.com/KIT-ISAS/lili-om.

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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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IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping

Cited by 50 publications

References 32 publications

Continuous Integration over SO(3) for IMU Preintegration

Continuous Integration over SO(3) for IMU Preintegration

Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping

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

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