Robust Odometry and Mapping for Multi-LiDAR Systems With Online Extrinsic Calibration

Jiao, Jianhao; Ye, Haoyang; Zhu, Yilong; Liu, Ming

doi:10.1109/tro.2021.3078287

Cited by 85 publications

(38 citation statements)

References 99 publications

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“…Focusing on LiDAR SLAM, similarly to LOAM and its variants [11], [12], methods like LIO-SAM [13], LIO-MAPPING [14], HDL-SLAM [3] also estimate the robot poses and a 3D map, but with option of integrating additional sensor modalities such as IMU and GPS. Other methods such as like LIMO [15], LIRO [16] and LVI-SLAM [17] fuse visual and LiDAR measurements for simultaneous localization and mapping.…”

Section: Related Workmentioning

confidence: 99%

Situational Graphs for Robot Navigation in Structured Indoor Environments

Bavle¹,

Sánchez-López²,

Shaheer³

et al. 2022

Preprint

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Autonomous mobile robots should be aware of their situation, understood as a comprehensive understanding of the environment along with the estimation of its own state, to successfully make decisions and execute tasks in natural environments. 3D scene graphs are an emerging field of research with great potential to represent these situations in a joint model comprising geometric, semantic and relational/topological dimensions. Although 3D scene graphs have already been utilized for this, further research is still required to effectively deploy them on-board mobile robots.To this end, we present in this paper a real-time online built Situational Graphs (S-Graphs), composed of a single graph representing the environment, while simultaneously improving the robot pose estimation. Our method utilizes odometry readings and planar surfaces extracted from 3D LiDAR scans, to construct and optimize in real-time a three layered S-Graph that includes a robot tracking layer where the robot poses are registered, a metric-semantic layer with features such as planar walls and our novel topological layer constraining higher-level features such as corridors and rooms. Our proposal does not only demonstrate state-of-the-art results for pose estimation of the robot, but also contributes with a metric-semantictopological model of the environment.

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Section: Related Workmentioning

confidence: 99%

Situational Graphs for Robot Navigation in Structured Indoor Environments

Bavle¹,

Sánchez-López²,

Shaheer³

et al. 2022

Preprint

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“…The selected informative segments are fully excited with general motion, making the calibrated parameters observable and solvable. Jiao et al [33] designed a sliding windowbased multi-LiDAR odometry system with the capability of online extrinsic calibration between multiple LiDARs, and the singular values of Hessian matrix are leveraged to examine the convergence of rotational extrinsic calibration.…”

Section: Observability Awareness In Calibrationmentioning

confidence: 99%

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

Lv¹,

Zuo²,

Hu³

et al. 2022

Preprint

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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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“…In this system, the two lidars are mounted at 180 degrees to each other to make up for the self-similar areas with low lidar observability. The authors of [32] proposed a system to achieve robust and simultaneous extrinsic calibration, odometry, and mapping for multiple lidars, while [33] proposed a scheme to combine multiple lidars with complementary FOV for feature-based lidar-inertia odometry and mapping. While the above methods can work well in single-scene applications, their adaptability to different environments was not considered, especially the environments that need to perceive height information.…”

Section: Introductionmentioning

confidence: 99%

High-Precision SLAM Based on the Tight Coupling of Dual Lidar Inertial Odometry for Multi-Scene Applications

Xiao

Liu

et al. 2022

Applied Sciences

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Simultaneous Localization and Mapping (SLAM) is an essential feature in many applications of mobile vehicles. To solve the problem of poor positioning accuracy, single use of mapping scene, and unclear structural characteristics in indoor and outdoor SLAM, a new framework of tight coupling of dual lidar inertial odometry is proposed in this paper. Firstly, through external calibration and an adaptive timestamp synchronization algorithm, the horizontal and vertical lidar data are fused, which compensates for the narrow vertical field of view (FOV) of the lidar and makes the characteristics of vertical direction more complete in the mapping process. Secondly, the dual lidar data is tightly coupled with an Inertial Measurement Unit (IMU) to eliminate the motion distortion of the dual lidar odometry. Then, the value of the lidar odometry after correcting distortion and the pre-integrated value of IMU are used as constraints to establish a non-linear least-squares objective function. Joint optimization is then performed to obtain the best value of the IMU state values, which will be used to predict the state of IMU at the next time step. Finally, experimental results are presented to verify the effectiveness of the proposed method.

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Robust Odometry and Mapping for Multi-LiDAR Systems With Online Extrinsic Calibration

Cited by 85 publications

References 99 publications

Situational Graphs for Robot Navigation in Structured Indoor Environments

Situational Graphs for Robot Navigation in Structured Indoor Environments

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

High-Precision SLAM Based on the Tight Coupling of Dual Lidar Inertial Odometry for Multi-Scene Applications

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