A single-shot pose estimation approach for a 2D laser rangefinder

Li, Yicheng; Hu, Zhaozheng; Li, Zhixiong; Cai, Yingfeng; Sun, Shuaishuai; Zhou, Junjie

doi:10.1088/1361-6501/ab455a

Cited by 4 publications

(4 citation statements)

References 31 publications

(45 reference statements)

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“…Because the road is close to the MLS, its surface points account for a large part of MLS data [46,47]. In the rotating mirror scanning mode commonly assembled in MLS systems [48], the spacing between the laser points in the horizontal plane increases with the measurement distance. Assuming that the closest projection point of the scanner to the ground (hereinafter referred to as the scanner ground track) is located on a flat road, it is located at the position with the highest density on the road.…”

Section: Methodsmentioning

confidence: 99%

Recovering Missing Trajectory Data for Mobile Laser Scanning Systems

et al. 2020

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Trajectory data are often used as important auxiliary information in preprocessing and extracting the target from mobile laser scanning data. However, the trajectory data stored independently may be lost and destroyed for various reasons, making the data unavailable for the relevant models. This study proposes recovering the trajectory of the scanner from point cloud data following the scanning principles of a rotating mirror. Two approaches are proposed from different input conditions: Ordered three-dimensional coordinates of point cloud data, with and without acquisition time. We recovered the scanner’s ground track through road point density analysis and restored the position of the center of emission of the laser based on plane reconstruction on a single scanning line. The validity and reliability of the proposed approaches were verified in the four typical urban, rural, winding, and viaduct road environments using two systems from different manufacturers. The result deviations of the ground track and scanner trajectory from their actual position were a few centimeters and less than 1 decimeter, respectively. Such an error is sufficiently small for the trajectory data to be used in the relevant algorithms.

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Section: Methodsmentioning

confidence: 99%

Recovering Missing Trajectory Data for Mobile Laser Scanning Systems

et al. 2020

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“…The choice of appropriate sensors is the foundation of robust localization. Commonly used sensors for robot localization include wireless or radio-frequency-identification receivers [9,10], cameras [11], INS/GPS [12,13], LIDAR [8,[14][15][16], etc. However, GPS is not suitable for indoor applications, the accuracy of WiFi-based localization is insufficient, and localization methods using RFID or reflectors require additional arrangement and maintenance in large-scale scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…if edge is an arc (16) where v max and v n refer to the maximum velocity and the current velocity of edge n, respectively. D n is the edge length, a is the acceleration, r n and α n are the radius and central angle of the arc, and v n 2 /a is the minimum distance required to ensure that the robot can accelerate to v n and stop at the next station.…”

Section: Efficient Trajectory Planningmentioning

confidence: 99%

A safe and efficient LIDAR-based navigation system for 4WS4WD mobile manipulators in manufacturing plants

Meng

Wang

Xie

et al. 2021

Meas. Sci. Technol.

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The safe and efficient autonomous navigation of mobile manipulators is still challenging in harsh manufacturing environments with dynamic obstacles and narrow spaces. This paper addresses that challenge by proposing an industrial-grade LIDAR-based navigation system for four-wheel-steering and four-wheel-driving mobile manipulators and focuses on enhancing the system’s safety and efficiency in manufacturing plants. On one hand, using an efficient soft-evidence unscented distance filter, a robust localization method is presented to eliminate dynamic interference. The variation results of scan matching are ingeniously applied to formulate a practical localization-failure-detection strategy, therefore improving localization safety under confusing working conditions. On the other hand, an efficient path-graph-based trajectory-planning method is proposed, which can instantly generate a trajectory in a safe and effective search space. Considering the regional connectivity and movement characteristics of the 4WS4WD mobile manipulator, a novel path graph is designed to construct the search space. Even in a large-scale scenario, a robot can obtain the desired trajectory within 5 ms. Ultimately, some core modules, such as a safety module and a fault-tolerant control module are integrated to form a comprehensive navigation system. The simulated and real experimental results prove the excellent performance of our navigation system, which has been successfully applied in many real-world scenarios.

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“…With the advent of computer science and the development of efficient image processing, visual pedestrian recognition has gained considerable attention, and subsequent analysis has been very popular, with an astounding degree of success. In recent years, adoption of deep learning methodologies has prevailed over the existing pedestrian recognition, and cyclist/motorcyclist detection [4]. Pioneered studies have addressed the detection of pedestrians and cyclists [5].…”

Section: Introductionmentioning

confidence: 99%

Intelligent co-detection of cyclists and motorcyclists based on an improved deep learning method

Xu¹,

Ma²,

Sarkodie-Gyan³

et al. 2020

Meas. Sci. Technol.

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Vulnerable road user (VRU) collision avoidance is a challenge task in developing an advanced driving assistant system (ADAS). This is because motorcyclists are the most VRUs, while most existing ADAS systems are not capable of distinguishing motorcyclists from cyclists. In order to address this problem, this study proposes a new detection method based on a center and scale prediction-proposal region (CSP-PR) improved deep learning model. This new method attempts to identify the individual difference and similar appearance between cyclists and motorcyclists from different perspectives; by doing so, the cyclists and motorcyclists can be co-detected from each other to enable effective VRU collision avoidance. For this purpose, a shared salient region extraction method is first introduced based on the CSP-PR; then multiple-instance target candidate regions are generated based on the redundancy strategy; last, a multi-task learning method is proposed to train an ResNet50-deep neural network to reduce the risk of overfitting and improve the co-detection rate of the cyclists and motorcyclists. In order to verify the effectiveness of the proposed CSP-PR-ResNet50 method, a local motorcyclist database and the Tsinghua-Daimler cyclist database were combined to form an experimental dataset. The experimental results demonstrate that the proposed method is able to effectively identify the cyclists and the motorcyclists. Compared with popular algorithms, including the faster region-based convolutional neural networks, YOLOv3 (You Only Look Once v3) and single shot multibox detector, the average precision (AP) of the cyclist detection produced by the proposed method is improved by 3.69%, 17.74% and 31.22%, respectively; and the AP of the motorcyclists detection is improved by 4.29%, 15.5% and 32.57%, respectively.

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A single-shot pose estimation approach for a 2D laser rangefinder

Cited by 4 publications

References 31 publications

Recovering Missing Trajectory Data for Mobile Laser Scanning Systems

Recovering Missing Trajectory Data for Mobile Laser Scanning Systems

A safe and efficient LIDAR-based navigation system for 4WS4WD mobile manipulators in manufacturing plants

Intelligent co-detection of cyclists and motorcyclists based on an improved deep learning method

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