Motion Guided LiDAR-Camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles

Castorena, Juan; Puskorius, G.V.; Pandey, Gaurav

doi:10.1007/s10846-020-01233-w

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…With constraints between the motions of the individual sensors given, they jointly optimize the extrinsic parameter and reduce the pose observation error using the Gauss-Helmert paradigm. Castorena et al (2020) proposed a motion-guided method for automatic calibration of the two multi-modal sensors. With a sequence of timesynchronized point clouds from LiDAR and the corresponding images from the camera, they compute the motion vector for each modality independently, then estimated the extrinsic parameter.…”

Section: Other Methodsmentioning

confidence: 99%

Automatic targetless LiDAR–camera calibration: a survey

Xiao

Wang³

et al. 2022

Artif Intell Rev

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The recent trend of fusing complementary data from LiDARs and cameras for more accurate perception has made the extrinsic calibration between the two sensors critically important. Indeed, to align the sensors spatially for proper data fusion, the calibration process usually involves estimating the extrinsic parameters between them. Traditional LiDAR-camera calibration methods often depend on explicit targets or human intervention, which can be prohibitively expensive and cumbersome. Recognizing these weaknesses, recent methods usually adopt the autonomic targetless calibration approach, which can be conducted at a much lower cost. This paper presents a thorough review of these automatic targetless LiDAR-camera calibration methods. Specifically, based on how the potential cues in the environment are retrieved and utilized in the calibration process, we divide the methods into four categories: information theory based, feature based, ego-motion based, and learning based methods. For each category, we provide an in-depth overview with insights we have gathered, hoping to serve as a potential guidance for researchers in the related fields.

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Section: Other Methodsmentioning

confidence: 99%

Automatic targetless LiDAR–camera calibration: a survey

Xiao

Wang³

et al. 2022

Artif Intell Rev

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“…Our SR approach is inspired by the work of Castorena et al [52] on the fusion of terrestrial LiDAR data with optical imagery. It involves reconstructing a sparse depth map by minimizing the sum of its squared directional gradients (SSDGs).…”

Section: (B) Propagation Of the Projected Valuesmentioning

confidence: 99%

“…In other topics, the study of SR applied to LiDAR depth measurements is also very active. Indeed, a reliable SR would benefit many applications, such as calibration for autonomous driving [52] or land cover classification [64].…”

Section: Relevance Of the Super-resolutionmentioning

confidence: 99%

Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image

et al. 2020

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Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such a challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution Light Detection and Ranging (LiDAR)-based elevation images—called z-images—generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%.

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“…On the input side, its performance can be enhanced by refining Lidar and visual odometry [15], [25] and optimizing timing offsets [26], [27]. From the perspective of outputs, some hybrid calibration methods combine HECalib with appearance-based methods [27]- [29], because HECalib can provide an initial extrinsic matrix for locally convergent appearance-based algorithms. However, they are ineffective when the initial calibration is poor, and their generalization ability is limited by appearance-based methods.…”

Section: Introductionmentioning

confidence: 99%

Targetless Lidar-camera Calibration via Cross-modality Structure Consistency

2023

Preprint

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Hand-eye calibration is popular in targetless sensor calibration, as it just requires the motion information of each sensor. However, hand-eye calibration is not applicable in degenerate scenes, where sensor motion lacks sufficient rotation. In this article, we propose a novel motion-based method to address this problem. The robustness and accuracy of our method are verified on six sequences of the KITTI Odometry dataset. Furthermore, our codes will be public on https://github.com/gitouni/Spatial-Temporal-LiDAR-camera-Calibration after acceptance.

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Motion Guided LiDAR-Camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles

Cited by 13 publications

References 14 publications

Automatic targetless LiDAR–camera calibration: a survey

Automatic targetless LiDAR–camera calibration: a survey

Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image

Targetless Lidar-camera Calibration via Cross-modality Structure Consistency

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