Photometric laser scanner to camera calibration for low resolution sensors

Gräter, Johannes; Strauß, Tobias; Lauer, Martin

doi:10.1109/itsc.2016.7795764

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…In contrast, the usage of scene corners avoids the need for special calibration objects (GomezOjeda et al, 2015) which offers potential to be extended to a self-calibration technique. Gräter et al (2016) follow a specific approach. They mention that many cameras are also sensible to electromagnetic radiation in the wavelengths emitted by laserscanners.…”

Section: Related Workmentioning

confidence: 99%

UCalMiCeL – UNIFIED INTRINSIC AND EXTRINSIC CALIBRATION OF A MULTI-CAMERA-SYSTEM AND A LASERSCANNER

Hillemann

Jutzi²

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Unmanned Aerial Vehicle (UAV) with adequate sensors enable new applications in the scope between expensive, large-scale, aircraftcarried remote sensing and time-consuming, small-scale, terrestrial surveyings. To perform these applications, cameras and laserscanners are a good sensor combination, due to their complementary properties. To exploit this sensor combination the intrinsics and relative poses of the individual cameras and the relative poses of the cameras and the laserscanners have to be known. In this manuscript, we present a calibration methodology for the Unified Intrinsic and Extrinsic Calibration of a Multi-Camera-System and a Laserscanner (UCalMiCeL). The innovation of this methodology, which is an extension to the calibration of a single camera to a line laserscanner, is an unifying bundle adjustment step to ensure an optimal calibration of the entire sensor system. We use generic camera models, including pinhole, omnidirectional and fisheye cameras. For our approach, the laserscanner and each camera have to share a joint field of view, whereas the fields of view of the individual cameras may be disjoint. The calibration approach is tested with a sensor system consisting of two fisheye cameras and a line laserscanner with a range measuring accuracy of 30mm. We evaluate the estimated relative poses between the cameras quantitatively by using an additional calibration approach for Multi-Camera-Systems based on control points which are accurately measured by a motion capture system. In the experiments, our novel calibration method achieves a relative pose estimation with a deviation below 1.8• and 6.4mm.

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Section: Related Workmentioning

confidence: 99%

UCalMiCeL – UNIFIED INTRINSIC AND EXTRINSIC CALIBRATION OF A MULTI-CAMERA-SYSTEM AND A LASERSCANNER

Hillemann

Jutzi²

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Since the LIDAR measures in a different domain, an additional step has to be added that combines feature tracking and the depth measured by the LIDAR. Moreover, LIDAR to camera calibration is still an active field of research ( [13], [14]) and its accuracy is limited to a few pixels. As a result, VSLAM with LIDAR and a monocular camera is an unattended topic, mainly covered by Zhang et al [15], [16] and Caselitz et al [17].…”

Section: Introduction and Related Workmentioning

confidence: 99%

LIMO: Lidar-Monocular Visual Odometry

Graeter

Wilczynski

Lauer

2018

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

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196

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Higher level functionality in autonomous driving depends strongly on a precise motion estimate of the vehicle. Powerful algorithms have been developed. However, their great majority focuses on either binocular imagery or pure LIDAR measurements. The promising combination of camera and LI-DAR for visual localization has mostly been unattended. In this work we fill this gap, by proposing a depth extraction algorithm from LIDAR measurements for camera feature tracks and estimating motion by robustified keyframe based Bundle Adjustment. Semantic labeling is used for outlier rejection and weighting of vegetation landmarks. The capability of this sensor combination is demonstrated on the competitive KITTI dataset, achieving a placement among the top 15. The code is released to the community.

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“…It was later observed that the performance can be enhanced by learning a compensation of the calibration bias through a deformation field [14]. LIght Detection And Ranging sensor LIDAR-camera calibration is also an expanding topic of research [15], [16] with accuracy reaching a few pixels. Previous work has been done with VSLAM and LIDAR [17]- [20].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization

Sehgal

Singandhupe

et al. 2019

Advances in Visual Computing

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Lidar-Monocular Visual Odometry (LIMO), a odometry estimation algorithm, combines camera and LIght Detection And Ranging sensor (LIDAR) for visual localization by tracking camera features as well as features from LIDAR measurements, and it estimates the motion using Bundle Adjustment based on robust key frames. For rejecting the outliers, LIMO uses semantic labelling and weights of the vegetation landmarks. A drawback of LIMO as well as many other odometry estimation algorithms is that it has many parameters that need to be manually adjusted according to the dynamic changes in the environment in order to decrease the translational errors. In this paper, we present and argue the use of Genetic Algorithm to optimize parameters with reference to LIMO and maximize LIMO's localization and motion estimation performance. We evaluate our approach on the well known KITTI odometry dataset and show that the genetic algorithm helps LIMO to reduce translation error in different datasets.

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Photometric laser scanner to camera calibration for low resolution sensors

Cited by 4 publications

References 11 publications

UCalMiCeL – UNIFIED INTRINSIC AND EXTRINSIC CALIBRATION OF A MULTI-CAMERA-SYSTEM AND A LASERSCANNER

UCalMiCeL – UNIFIED INTRINSIC AND EXTRINSIC CALIBRATION OF A MULTI-CAMERA-SYSTEM AND A LASERSCANNER

LIMO: Lidar-Monocular Visual Odometry

Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization

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