Front-end matching optimised algorithm of cartographer with multi-resolution layered search strategy

Di, Yiqun; Ma, Xianghua

doi:10.1504/ijmic.2022.125544

Cited by 2 publications

(1 citation statement)

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“…After the establishment of the map, to achieve the robot navigation function, the robot's driving route needs to be planned, mainly consisting of global and local path planning [18].…”

Section: Path Planningmentioning

confidence: 99%

Research on a Real-Time Monitoring System for Campus Woodland Fires via Deep Learning

Xu,

Chen,

et al. 2024

Forests

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To solve the problems of low recognition accuracy and large amounts of computation required in forest fire detection algorithms, this paper, aiming to make improvements in these two aspects, proposes a G-YOLOv5n-CB forest fire detection algorithm based on the YOLOv5 algorithm and develops a set of real-time fire monitoring systems applicable to campus forest land with the aid of deep learning technology. The system employs an unmanned vehicle to navigate automatically and collect image information through a camera and deploys its algorithm on the unmanned vehicle’s Jetson Nano hardware platform. The results demonstrate that the proposed YOLOv5n-CB algorithm increased the mAP value index by 1.4% compared with the original algorithm on the self-made forest fire dataset. The improved G-YOLOv5n-CB model was deployed on the Jetson Nano platform for testing, and its detection speed reached 15 FPS. It can accurately detect and display real-time forest fires on campus and has, thus, a high application value.

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“…After the establishment of the map, to achieve the robot navigation function, the robot's driving route needs to be planned, mainly consisting of global and local path planning [18].…”

Section: Path Planningmentioning

confidence: 99%

Research on a Real-Time Monitoring System for Campus Woodland Fires via Deep Learning

Xu,

Chen,

et al. 2024

Forests

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Optimize data association of point cloud to improve the quality of mapping and positioning

Zhou,

Quan,

Huang

et al. 2024

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Purpose Accurate mapping is crucial for the positioning and navigation of mobile robots. Recent advancements in algorithms and the accuracy of LiDAR sensors have led to a gradual improvement in map quality. However, challenges such as lag in closing loops and vignetting at map boundaries persist due to the discrete and sparse nature of raster map data. The purpose of this study is to reduce the error of map construction and improve the timeliness of closed loop. Design/methodology/approach In this letter, the authors introduce a method for dynamically adjusting point cloud distance constraints to optimize data association (ODA-d), effectively addressing these issues. The authors propose a dynamic threshold optimization method for matching point clouds to submaps during scan matching. Findings Large deviations in LiDAR sensor point cloud data, when incorporated into the submap, can result in irreparable errors in correlation matching and loop closure optimization. By implementing a data association framework with double constraints and dynamically adjusting the matching threshold, the authors significantly enhance submap quality. In addition, the authors introduce a dynamic fusion method that accounts for both submap size and the distance between submaps during the mapping process. ODA-d reduces errors between submaps and facilitates timely loop closure optimization. Originality/value The authors validate the localization accuracy of ODA-d by examining translation and rotation errors across three open data sets. Moreover, the authors compare the quality of map construction in a real-world environment, demonstrating the effectiveness of ODA-d.

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Front-end matching optimised algorithm of cartographer with multi-resolution layered search strategy

Cited by 2 publications

References 0 publications

Research on a Real-Time Monitoring System for Campus Woodland Fires via Deep Learning

Research on a Real-Time Monitoring System for Campus Woodland Fires via Deep Learning

Optimize data association of point cloud to improve the quality of mapping and positioning

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