Extrinsic Calibration of Camera and 2D Laser Sensors without Overlap

Yousef, Khalil M. Ahmad; Mohd, Bassam Jamil; Al-Widyan, Khalid; Hayajneh, Thaier

doi:10.3390/s17102346

Cited by 20 publications

(11 citation statements)

References 35 publications

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“…(a) Taylor et al [26] (b) Ahmad et al [9] (c) Our solution at the coarse stage (d) Our solution at the ref nement stage Fig. 9.…”

Section: ) Experiments With Simulated Datamentioning

confidence: 92%

“…Despite recent developments, fusing multimodal sensory information is still a challenging problem [7]. Previously proposed solutions can provide a reasonable estimation of these parameters but they are limited due to their offline processing requirement [8] and a dedicated artificial calibration target present in a controlled environment [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Park

Moghadam

Kim

et al. 2020

IEEE Robot. Autom. Lett.

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The demand for multimodal sensing systems for robotics is growing due to the increase in robustness, reliability and accuracy offered by these systems. These systems also need to be spatially and temporally co-registered to be effective. In this paper, we propose a targetless and structureless spatiotemporal camera-LiDAR calibration method. Our method combines a closed-form solution with a modified structureless bundle adjustment where the coarse-to-fine approach does not require an initial guess on the spatiotemporal parameters. Also, as 3D features (structure) are calculated from triangulation only, there is no need to have a calibration target or to match 2D features with the 3D point cloud which provides flexibility in the calibration process and sensor configuration. We demonstrate the accuracy and robustness of the proposed method through both simulation and real data experiments using multiple sensor payload configurations mounted to hand-held, aerial and legged robot systems. Also, qualitative results are given in the form of a colorized point cloud visualization.

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“…(a) Taylor et al [26] (b) Ahmad et al [9] (c) Our solution at the coarse stage (d) Our solution at the ref nement stage Fig. 9.…”

Section: ) Experiments With Simulated Datamentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Park

Moghadam

Kim

et al. 2020

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

show abstract

“…Multi-sensor fusion can solve these problems and improve the accuracy of system positioning and environment mapping.VI-SLAM technology is an example of multi-sensor fusion. Research and applications involving multi-sensor fusion in SLAM are expected to grow, as evidenced by [123,124].…”

Section: Hardware Integration and Multi-sensor Fusionmentioning

confidence: 99%

A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

et al. 2018

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Visual-inertial simultaneous localization and mapping (VI-SLAM) is popular research topic in robotics. Because of its advantages in terms of robustness, VI-SLAM enjoys wide applications in the field of localization and mapping, including in mobile robotics, self-driving cars, unmanned aerial vehicles, and autonomous underwater vehicles. This study provides a comprehensive survey on VI-SLAM. Following a short introduction, this study is the first to review VI-SLAM techniques from filtering-based and optimization-based perspectives. It summarizes state-of-the-art studies over the last 10 years based on the back-end approach, camera type, and sensor fusion type. Key VI-SLAM technologies are also introduced such as feature extraction and tracking, core theory, and loop closure. The performance of representative VI-SLAM methods and famous VI-SLAM datasets are also surveyed. Finally, this study contributes to the comparison of filtering-based and optimization-based methods through experiments. A comparative study of VI-SLAM methods helps understand the differences in their operating principles. Optimization-based methods achieve excellent localization accuracy and lower memory utilization, while filtering-based methods have advantages in terms of computing resources. Furthermore, this study proposes future development trends and research directions for VI-SLAM. It provides a detailed survey of VI-SLAM techniques and can serve as a brief guide to newcomers in the field of SLAM and experienced researchers looking for possible directions for future work.

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“…These kinds of algorithms require the extrinsic calibration of different sensors [ 21 ]. Due to the autonomous vehicles navigating in a dynamic environment and LiDAR sensors providing a low data update rate, multi-LiDAR systems need careful spatial and temporal synchronization.…”

Section: Introductionmentioning

confidence: 99%

A Pedestrian Detection Algorithm Based on Score Fusion for Multi-LiDAR Systems

et al. 2021

Sensors

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Pedestrian detection plays an essential role in the navigation system of autonomous vehicles. Multisensor fusion-based approaches are usually used to improve detection performance. In this study, we aimed to develop a score fusion-based pedestrian detection algorithm by integrating the data of two light detection and ranging systems (LiDARs). We first evaluated a two-stage object-detection pipeline for each LiDAR, including object proposal and fine classification. The scores from these two different classifiers were then fused to generate the result using the Bayesian rule. To improve proposal performance, we applied two features: the central points density feature, which acts as a filter to speed up the process and reduce false alarms; and the location feature, including the density distribution and height difference distribution of the point cloud, which describes an object’s profile and location in a sliding window. Extensive experiments tested in KITTI and the self-built dataset show that our method could produce highly accurate pedestrian detection results in real-time. The proposed method not only considers the accuracy and efficiency but also the flexibility for different modalities.

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Extrinsic Calibration of Camera and 2D Laser Sensors without Overlap

Cited by 20 publications

References 35 publications

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

A Pedestrian Detection Algorithm Based on Score Fusion for Multi-LiDAR Systems

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