Structure-From-Motion in 3D Space Using 2D Lidars

Choi, Dong Geol; Bok, Yunsu; Kim, Jun-Sik; Shim, Inwook; Kweon, In So

doi:10.3390/s17020242

Cited by 11 publications

(12 citation statements)

References 32 publications

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“…We carried out both simulated and real experiments to validate the performance of the proposed method. To measure the error of head-eye calibration, we computed the difference (

) between the reference transformation (

) and the estimated transformation (

), as in [ 24 ]. For

, the orientation error

was calculated in terms of angular error using Rodrigues’s rotation formula [ 25 ], and the translation error was measured by

in terms of absolute length.…”

Section: Resultsmentioning

confidence: 99%

Stereo Camera Head-Eye Calibration Based on Minimum Variance Approach Using Surface Normal Vectors

Lee¹,

Jeong

2018

Sensors

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This paper presents a stereo camera-based head-eye calibration method that aims to find the globally optimal transformation between a robot’s head and its eye. This method is highly intuitive and simple, so it can be used in a vision system for humanoid robots without any complex procedures. To achieve this, we introduce an extended minimum variance approach for head-eye calibration using surface normal vectors instead of 3D point sets. The presented method considers both positional and orientational error variances between visual measurements and kinematic data in head-eye calibration. Experiments using both synthetic and real data show the accuracy and efficiency of the proposed method.

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“…We carried out both simulated and real experiments to validate the performance of the proposed method. To measure the error of head-eye calibration, we computed the difference (

) between the reference transformation (

) and the estimated transformation (

), as in [ 24 ]. For

, the orientation error

was calculated in terms of angular error using Rodrigues’s rotation formula [ 25 ], and the translation error was measured by

in terms of absolute length.…”

Section: Resultsmentioning

confidence: 99%

Stereo Camera Head-Eye Calibration Based on Minimum Variance Approach Using Surface Normal Vectors

Lee¹,

Jeong

2018

Sensors

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“…Different studies have addressed the problem of object segmentation on LiDAR point clouds [25,28,29,54,55]; however, they either used 3D point clouds or assumed that the world consists of separate objects that are not physically overlapping with each other. In this paper, we address the challenge of having multiple dynamic and interacting/overlapping objects.…”

Section: Grouping Of Lidar Measurements Into Clusters With Unique Idsmentioning

confidence: 99%

Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles

Khatab

Onsy

Abouelfarag

2022

Sensors

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One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due to their continuously changing behavior. The majority of commercially available AVs, and research into them, depends on employing expensive sensors. However, this hinders the development of further research on the operations of AVs. In this paper, therefore, we focus on the use of a lower-cost single-beam LiDAR in addition to a monocular camera to achieve multiple 3D vulnerable object detection in real driving scenarios, all the while maintaining real-time performance. This research also addresses the problems faced during object detection, such as the complex interaction between objects where occlusion and truncation occur, and the dynamic changes in the perspective and scale of bounding boxes. The video-processing module works upon a deep-learning detector (YOLOv3), while the LiDAR measurements are pre-processed and grouped into clusters. The output of the proposed system is objects classification and localization by having bounding boxes accompanied by a third depth dimension acquired by the LiDAR. Real-time tests show that the system can efficiently detect the 3D location of vulnerable objects in real-time scenarios.

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“…to perform various robotics tasks in complex environment as shown in Tables VIII to X. (www.clearpathrobotics.com/2017/01/3dlidar-true-3d-sensing-spinning-2d-alternatives/; www.hokuyoaut.jp/;https://velodynelidar.com/; Choi et al, 2017).…”

Section: Laser Scannermentioning

confidence: 99%

Comparative analysis of range sensors for the robust autonomous navigation – a review

Singh

Nagla

2019

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Purpose The purpose of this research is to provide the necessarily and resourceful information regarding range sensors to select the best fit sensor for robust autonomous navigation. Autonomous navigation is an emerging segment in the field of mobile robot in which the mobile robot navigates in the environment with high level of autonomy by lacking human interactions. Sensor-based perception is a prevailing aspect in the autonomous navigation of mobile robot along with localization and path planning. Various range sensors are used to get the efficient perception of the environment, but selecting the best-fit sensor to solve the navigation problem is still a vital assignment. Design/methodology/approach Autonomous navigation relies on the sensory information of various sensors, and each sensor relies on various operational parameters/characteristic for the reliable functioning. A simple strategy shown in this proposed study to select the best-fit sensor based on various parameters such as environment, 2 D/3D navigation, accuracy, speed, environmental conditions, etc. for the reliable autonomous navigation of a mobile robot. Findings This paper provides a comparative analysis for the diverse range sensors used in mobile robotics with respect to various aspects such as accuracy, computational load, 2D/3D navigation, environmental conditions, etc. to opt the best-fit sensors for achieving robust navigation of autonomous mobile robot. Originality/value This paper provides a straightforward platform for the researchers to select the best range sensor for the diverse robotics application.

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Structure-From-Motion in 3D Space Using 2D Lidars

Cited by 11 publications

References 32 publications

Stereo Camera Head-Eye Calibration Based on Minimum Variance Approach Using Surface Normal Vectors

Stereo Camera Head-Eye Calibration Based on Minimum Variance Approach Using Surface Normal Vectors

Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles

Comparative analysis of range sensors for the robust autonomous navigation – a review

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