Efficient re-localization of mobile robot using strategy of finding a missing person

Meng, Jie; Wang, Zhen; Xie, Yuanlong; Jiang, Laizhu; Li, Gen; Liu, Chao

doi:10.1016/j.measurement.2021.109212

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…The result is the weight at t -1 multiplied by a ratio which is gained from the importance sampling step at time t. In the resampling phase, the probability of drawing a sample is proportional to its weight. The newly sampled particles are usually set to the same weight, such as 1 N . When the mobile robot continues to move forward, detects and recognizes another room number as the text landmark, the importance weights w will be updated according to Figure 7 and Equations ( 18)- (20).…”

Section: Localization Stagementioning

confidence: 99%

“…As a fundamental capability of an autonomous mobile robot, localization can assist the robot in knowing where it is. In addition, it is prior knowledge for path planning when the mobile robot performs a navigation task [1]. In an indoor environment where a global positioning system (GPS) signal is denied, the active perception method usually builds a map by employing SLAM techniques in advance [2,3], then the mobile robot localizes itself according to the map.…”

Section: Introductionmentioning

confidence: 99%

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Text-MCL: Autonomous Mobile Robot Localization in Similar Environment Using Text-Level Semantic Information

Zhang

Wang

et al. 2022

Machines

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Localization is one of the most important issues in mobile robotics, especially when an autonomous mobile robot performs a navigation task. The current and popular occupancy grid map, based on 2D LiDar simultaneous localization and mapping (SLAM), is suitable and easy for path planning, and the adaptive Monte Carlo localization (AMCL) method can realize localization in most of the rooms in indoor environments. However, the conventional method fails to locate the robot when there are similar and repeated geometric structures, like long corridors. To solve this problem, we present Text-MCL, a new method for robot localization based on text information and laser scan data. A coarse-to-fine localization paradigm is used for localization: firstly, we find the coarse place for global localization by finding text-level semantic information, and then get the fine local localization using the Monte Carlo localization (MCL) method based on laser data. Extensive experiments demonstrate that our approach improves the global localization speed and success rate to 96.2% with few particles. In addition, the mobile robot using our proposed approach can recover from robot kidnapping after a short movement, while conventional MCL methods converge to the wrong position.

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Section: Localization Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Text-MCL: Autonomous Mobile Robot Localization in Similar Environment Using Text-Level Semantic Information

Zhang

Wang

et al. 2022

Machines

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show abstract

“…The traditional monitoring method based on a fixed perspective can no longer meet people's increasing demand for security protection. Therefore, PTZ cameras with rotation, pan, and zoom functions have gradually become a focus of people's attention [5][6][7][8]. A camera with a PTZ function tracks and observes the target in the scene by changing its own parameters.…”

Section: Introductionmentioning

confidence: 99%

Cell-Based Target Localization and Tracking with an Active Camera

You

Xiao

et al. 2022

Applied Sciences

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This paper proposes a new method of target localization and tracking. The method consists of four parts. The first part is to divide the scene into multiple cells based on the camera’s parameters and calibrate the position and error of each vertex. The second part mainly uses the bounding box detection algorithm, YOLOv4, based on deep learning to detect and recognize the scene image sequence and obtain the type, length, width, and position of the target to be tracked. The third part is to match each vertex of the cell in the image and the cell in the scene, generate a homography matrix, and then use the PnP model to calculate the precise world coordinates of the target in the image. In this process, a cell-based accuracy positioning method is proposed for the first time. The fourth part uses the proposed PTH model to convert the obtained world coordinates into P, T, and H values for the purpose of actively tracking and observing the target in the scene with a PTZ camera. The proposed method achieved precise target positioning and tracking in a 50 cm ∗ 250 cm horizontal channel and a vertical channel. The experimental results show that the method can accurately identify the target to be tracked in the scene, can actively track the moving target in the observation scene, and can obtain a clear image and accurate trajectory of the target. It is verified that the maximum positioning error of the proposed cell-based positioning method is 2.31 cm, and the average positioning error is 1.245 cm. The maximum error of the proposed tracking method based on the PTZ camera is 1.78 degrees, and the average error is 0.656 degrees.

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Efficient and accurate mapping method of underground metal mines using mobile mining equipment and solid-state lidar

Wang,

Peng

et al. 2023

Measurement

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Efficient re-localization of mobile robot using strategy of finding a missing person

Cited by 10 publications

References 35 publications

Text-MCL: Autonomous Mobile Robot Localization in Similar Environment Using Text-Level Semantic Information

Text-MCL: Autonomous Mobile Robot Localization in Similar Environment Using Text-Level Semantic Information

Cell-Based Target Localization and Tracking with an Active Camera

Efficient and accurate mapping method of underground metal mines using mobile mining equipment and solid-state lidar

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