The Road is Enough! Extrinsic Calibration of Non-overlapping Stereo Camera and LiDAR using Road Information

Jeong, Jin-Yong; Cho, Younghun; Kim, Ayoung

doi:10.1109/lra.2019.2921648

Cited by 45 publications

(22 citation statements)

References 24 publications

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“…The problem of estimation of the rigid body transformation between the multimodal sensory information (camera and LiDAR) has been extensively studied in the past two decades [1], [4]- [6]. Despite recent developments, fusing multimodal sensory information is still a challenging problem [7].…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Park

Moghadam

Kim

et al. 2020

IEEE Robot. Autom. Lett.

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“…targets. This is an interesting piece of future work because of the nonlinear operation required when projecting LiDAR points to the camera plane; see ( 6) and (7).…”

Section: Qualitative Results and Discussionmentioning

confidence: 99%

Improvements to Target-Based 3D LiDAR to Camera Calibration

Huang

Grizzle

2020

IEEE Access

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“…The studies of extrinsic calibration and the methodologies are well-established in the literature, see reference [ 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 ] for example. Though, the extrinsic calibration of multiple sensors with various physical measurement principles can pose a challenge in multi-sensor systems.…”

Section: Sensor Calibration and Sensor Fusion For Object Detectionmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong

Velasco-Hernandez

Barry

et al. 2021

Sensors

487

133

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With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems.

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The Road is Enough! Extrinsic Calibration of Non-overlapping Stereo Camera and LiDAR using Road Information

Cited by 45 publications

References 24 publications

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Improvements to Target-Based 3D LiDAR to Camera Calibration

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

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