Mobile robot positioning method based on multi-sensor information fusion laser SLAM

Liu, Zhixiang; Xie, Chunxue; Xie, Miao; Murai, Jun

doi:10.1007/s10586-018-2474-7

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…Among them, the centralized state fusion is the same as the centralized observation fusion algorithm; that is, the observation equations of all local sensors in the art design are merged into an augmented observation equation and then combined with the state equation to obtain a centralized global filter and finally obtain the global optimum untie. The distributed state fusion of art design is used to optimally weight the local filters corresponding to each sensor to obtain the distributed global filter and calculate it based on the local filter estimation error covariance matrix [20]. Multisensor data fusion will have a more sensitive response to the art design in the sampling set.…”

Section: Results Analysismentioning

confidence: 99%

Multisensor Information Fusion‐Assisted Intelligent Art Design under Wireless Virtual Reality Environment

Dong

2021

Journal of Sensors

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Under the background of intelligent technologies, art designers need to use information technology to assist the design of art factors and fully realize the integration of art design and information technology. Multisensor information fusion technology can more intuitively and visually carry out a more comprehensive grasp of the objectives to be designed, maximize the positive effects of art design, and achieve its overall optimization and can also help art designers get rid of the traditional monolithic and obsolete design concepts. Based on multisensor information fusion technology under wireless virtual reality environment, principles of signal acquisition and preprocessing, feature extraction, and fusion calculation, to analyze the information processing process of multisensor information fusion, conduct the model construction and performance evaluation for intelligent art design, and propose an intelligent art design model based on multisensor information fusion technology, we discuss the realization of multisensor information fusion algorithm in intelligent art design and finally carry out a simulation experiment and its result analysis by taking the environment design of a parent-child restaurant as an example. The study results show that using multisensor information fusion in the environmental design of parent-child restaurant is better than using a single sensor for that; at the same time, using force sensors has a better environmental design effect than using vibration sensors. The multisensor information fusion technology can automatically analyze the observation information of several sources obtained in time sequence under certain criteria and comprehensively perform information processing for the completion of the decision-making and estimation tasks required for intelligent art design.

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Section: Results Analysismentioning

confidence: 99%

Multisensor Information Fusion‐Assisted Intelligent Art Design under Wireless Virtual Reality Environment

Dong

2021

Journal of Sensors

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“…As shown in figure 2(a), the camera is horizontally placed on the left arm of the rotating platform. It is specified that the origin of the world coordinate frame O w is in the rotation axis of the scanner, the optical center of the depth image is the origin of the world coordinate frame, the two arms of the rotating platform are parallel to the X w axis of the world coordinate frame, and the light plane is vertical to the Z w O w X w plane of the world coordinate frame [7].…”

Section: System Principlementioning

confidence: 99%

The 3D reconstruction method of a line-structured light vision sensor based on composite depth images

Wang¹,

Zhehai²

2021

Meas. Sci. Technol.

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In order to simplify the three-dimensional (3D) reconstruction process of a rotating scanning line-structured light vision sensor, a 3D reconstruction method of composite depth images is proposed. First, a frame of an image acquired by a camera at a constant speed is converted into a column of data in the composite pixel image. Second, the composite pixel image is transformed into a depth image via coordinate transformation. Finally, the 3D coordinates of the object are calculated from the depth image by perspective transformation. The parameters of the light plane are calibrated by using a one-dimensional target, and the focal length of the depth image is calibrated by using a ball target. Compared with the traditional 3D reconstruction method of a structured light vision sensor, this method uses a composite depth image instead of point cloud splicing, which is simple to operate and flexible to calibrate, and further improves the efficiency of the 3D reconstruction of a structured light vision sensor. Experiments show that the absolute error of the 3D measurement in the measurement range of 380–4000 mm is less than 0.21 mm, and the accuracy is higher than 0.26%, which significantly improves the measurement efficiency and point cloud density of the system, and can meet the general 3D reconstruction requirements of the scene.

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“…Simultaneous localization and mapping (SLAM) [1] refers to the technology that the robot is placed in an unknown scene, and the robot can update the environmental map in real time through the equipped sensors, determine its own posture and locate independently. At present, the mainstream slams are divided into the following four categories: laser slam [2][3]; Visual slam [4][5]; Slam of multi-sensor fusion [6][7]; Slam combined with deep learning [8][9]. Visual slam has become a research hotspot because of its low cost and rich environmental information.…”

Section: Introductionmentioning

confidence: 99%

Research on ORB-SLAM3 algorithm adapting to dynamic environment

Wang

Yang

et al. 2023

Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022)

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The visual SLAM algorithm based on the assumption of static environment will be affected by the motion feature points in dynamic environment, resulting in the system robustness and accuracy degradation. To solve this problem, a visual SLAM algorithm for dynamic feature removal based on instance segmentation is proposed. Firstly, Yolact network is used to detect potential moving objects, and the obtained semantic information is combined with epipolar geometric constraints to accurately eliminate dynamic feature points. Considering the failure of example segmentation network, this algorithm combines the semantic information in the historical key frame with the depth map and uses the region growth algorithm to segment the dynamic objects that are not detected by Yolact, which further improves the robustness of the system. The experimental results show that compared with ORB-SLAM3, the absolute trajectory error and relative trajectory error of the algorithm in the dynamic environment are significantly reduced.

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Mobile robot positioning method based on multi-sensor information fusion laser SLAM

Cited by 8 publications

References 11 publications

Multisensor Information Fusion‐Assisted Intelligent Art Design under Wireless Virtual Reality Environment

Multisensor Information Fusion‐Assisted Intelligent Art Design under Wireless Virtual Reality Environment

The 3D reconstruction method of a line-structured light vision sensor based on composite depth images

Research on ORB-SLAM3 algorithm adapting to dynamic environment

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