Automatic extrinsic calibration for lidar-stereo vehicle sensor setups

Guindel, Carlos; Beltrán, Jorge Ramírez; Martín, David; García, Fernando

doi:10.1109/itsc.2017.8317829

Cited by 145 publications

(109 citation statements)

References 16 publications

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“…Three pairs of normal directions and checkerboard poses are enough to calculate the extrinsic parameters. By identifying multiple co-planner 3D positions from 3D pointclouds, Guindel et al [12] reduced the minimum number of data required for calibration. Many other variations of target based methods [8], [13] have been proposed to address the limitations of the Zhang [11] method, however, due to their requirement of dedicated artificial target objects for calibration, they are not suitable for in-situ calibration where such a calibration target is not available.…”

Section: Related Workmentioning

confidence: 99%

“…Temporal calibration is also an important part of multimodal sensor fusion but is often not addressed in previous literature for the reason that many of the systems are either aiming for static scanning [16] or short-term operation [18]. However, in many practical applications in robotics, the system continuously receives asynchronous estimations from LiDAR and a not connected or unsynchronised visible camera while in motion [12]. This is especially the case for hand-held systems where the angular velocity is rapidly changing.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

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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“…There are many methods to estimate the relative pose of a camera with respect to a lidar sensor [3], [4], [6]- [8], [10], [17]. Dhall et al [10] use a square plate as calibration target, which has an ArUco marker (a square black-white pattern) [18] to facilitate pose estimation in monocular camera.…”

Section: A Multi-modal Calibration With Lidar Camera and Radarmentioning

confidence: 99%

“…Up till now, existing calibration tools only addressed pairwise sensor calibrations of maximally two sensing modalities [3]- [12]. Since each modality has a different measurement principle, each proposed calibration procedure used different target designs.…”

Section: Introductionmentioning

confidence: 99%

An Extrinsic Calibration Tool for Radar, Camera and Lidar

Domhof

Julian

Dariu

2019

2019 International Conference on Robotics and Automation (ICRA)

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We present a novel open-source tool for extrinsic calibration of radar, camera and lidar. Unlike currently available offerings, our tool facilitates joint extrinsic calibration of all three sensing modalities on multiple measurements. Furthermore, our calibration target design extends existing work to obtain simultaneous measurements for all these modalities. We study how various factors of the calibration procedure affect the outcome on real multi-modal measurements of the target. Three different configurations of the optimization criterion are considered, namely using error terms for a minimal amount of sensor pairs, or using terms for all sensor pairs with additional loop closure constraints, or by adding terms for structure estimation in a probabilistic model. The experiments further evaluate how the number of calibration boards affect calibration performance, and robustness against different levels of zero mean Gaussian noise. Our results show that all configurations achieve good results for lidar to camera errors and that fully connected pose estimation shows the best performance for lidar to radar errors when more than five board locations are used.

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“…They require a specific calibration setup with multiple checkerboard targets. Simpler techniques that can solve for the LiDAR-camera extrinsic parameters, using simple easy-to-make targets and lesser number of correspondences have been recently proposed in [8], [9]. Although accurate, these techniques are slow, labor intensive, and require careful tuning of several hyperparameters.…”

Section: Related Workmentioning

confidence: 99%

CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks

Iyer

Ram

Murthy

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

153

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Fig. 1: CalibNet estimates the extrinsic calibration parameters between a 3D LiDAR and a 2D camera. It takes as input an RGB image (a) from a calibrated camera, a raw LiDAR point cloud (b), and outputs a 6-DoF rigid-body transformation T that best aligns the two inputs. (c) shows the colorized point cloud output for a mis-calibrated setup, and (d) shows the output after calibration using our network. As shown, using the mis-calibrated point cloud to recover a colorized 3D map of the world results in an incoherent reconstruction. Notice how the 3D structures highlighted in (c) using red rectangles fail to project to their 2D counterparts. However, using the extrinsic calibration parameters predicted by CalibNet produces more consistent and accurate reconstructions (d), even for large initial mis-calibrations.Abstract-3D LiDARs and 2D cameras are increasingly being used alongside each other in sensor rigs for perception tasks. Before these sensors can be used to gather meaningful data, however, their extrinsics (and intrinsics) need to be accurately calibrated, as the performance of the sensor rig is extremely sensitive to these calibration parameters. A vast majority of existing calibration techniques require significant amounts of data and/or calibration targets and human effort, severely impacting their applicability in large-scale production systems. We address this gap with CalibNet: a geometrically supervised deep network capable of automatically estimating the 6-DoF rigid body transformation between a 3D LiDAR and a 2D camera in real-time. CalibNet alleviates the need for calibration targets, thereby resulting in significant savings in calibration efforts. During training, the network only takes as input a LiDAR point cloud, the corresponding monocular image, and the camera calibration matrix K. At train time, we do not impose direct supervision (i.e., we do not directly regress to the calibration parameters, for example). Instead, we train the network to predict calibration parameters that maximize the geometric and photometric consistency of the input images and point clouds. CalibNet learns to iteratively solve the underlying geometric problem and accurately predicts extrinsic calibration parameters for a wide range of mis-calibrations, without requiring retraining or domain adaptation. The project page is hosted at https://epiception.github.io/CalibNet

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Automatic extrinsic calibration for lidar-stereo vehicle sensor setups

Cited by 145 publications

References 16 publications

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

An Extrinsic Calibration Tool for Radar, Camera and Lidar

CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks

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