False Detections Revising Algorithm for Millimeter Wave Radar SLAM in Tunnel

Li, Yang; Wei, Yonghui; Wang, Yanping; Lin, Yun; Shen, Wenjie; Wen, Jinyu

doi:10.3390/rs15010277

Cited by 7 publications

(9 citation statements)

References 28 publications

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“…Ref. [1] analyzed the impact of multipath clutter points on localization, improving pose accuracy by 20%, yet cumulative errors still exist. A new opportunity arises by reducing cumulative errors in long-term localization processes through multi-sensor fusion.…”

Section: Discussionmentioning

confidence: 99%

“…Based on [3], Ref. [1] incorporates scattering angle features, which reduces the influence of multipath clutter points on millimeter-wave radar odometry. Nevertheless, there is still an issue with pose drift due to the long-term accumulation of radar measurement errors.…”

Section: Related Workmentioning

confidence: 99%

“…Using millimeter-wave radar odometry, Remote Sens. 2024, 16, 114 2 of 26 they perceive the environment and obtain their positioning information, but they struggle to create closed loops [1].…”

Section: Introductionmentioning

confidence: 99%

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Correlative Scan Matching Position Estimation Method by Fusing Visual and Radar Line Features

Li,

Cui,

Wang

et al. 2023

Remote Sensing

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Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar’s simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Correlative Scan Matching Position Estimation Method by Fusing Visual and Radar Line Features

Li,

Cui,

Wang

et al. 2023

Remote Sensing

Self Cite

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“…Radar and Sonar Signal Processing: Radar and sonar systems use filtering methods to extract relevant information from complex signals. Filters help detect targets, remove clutter, and enhance signal-to-noise ratios in radar and sonar data [62].…”

Section: Other Application Fields Of Filtering Methodsmentioning

confidence: 99%

Filtering Methods for Biomedical Image Denoising

Zhao

2023

Preprint

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In this paper, the filtering method of biomedical image denoising is described comprehensively. Firstly, it introduces the biomedical image denoising, describes the relationship between biomedical image denoising and medical care, introduces the filtering methods, the filtering methods of biomedical image denoising, the challenges encountered by the current filtering methods, and other application fields of filtering methods. Firstly, the background of biomedical image denoising is introduced. Biomedical image denoising is a challenge. Different imaging modes have different noise characteristics, and noise levels can vary greatly depending on the specific application. Secondly, it describes that biomedical image denoising plays an important role in medical care, and the biomedical image directly affects the patient's diagnosis, treatment plan and the overall quality of medical care service. Then the filtering method is introduced in detail, describing the core concepts and related features of linear filtering, nonlinear filtering and frequency domain filtering, and then focusing on the adaptive filtering method, describing the characteristics, conditions of use, common algorithms and advantages of adaptive filtering method. Then the filter methods of biomedical image denoising are introduced, and the core concepts of Gaussian filter, median filter, total variation denoising and Wiener filter are introduced respectively. Then, the challenges encountered by filtering methods are described, such as the accurate selection of filters, the balance between noise reduction and image detail preservation are introduced. Finally, the application of filtering method in other fields is mentioned, such as audio processing, speech recognition and so on. In summary, this paper comprehensively expounds the denoising and filtering methods of biomedical images, the filtering methods of medical image denoising, the relationship between medical image denoising and medical care, and the challenges encountered by filtering methods.

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“…These false detections can interfere with the extraction of road boundary feature points and target detection, especially the impact on the seed points used during the road boundary feature point extraction process. The azimuth scattering angle is a range within which the radar can detect a target [25]. As shown in Figure 3, the azimuth scattering angle of the false detection point is denoted as θ21, while the azimuth scattering angle of the real target point is denoted as θ31.…”

Section: Rail Transit Scattering Analysis and False Detection Identif...mentioning

confidence: 99%

Long-Range Perception System for Road Boundaries and Objects Detection in Trains

Pan

Fan

et al. 2023

Remote Sensing

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This article introduces a long-range sensing system based on millimeter-wave radar, which is used to detect the roadside boundaries and track trains for trains. Due to the high speed and long braking distance of trains, existing commercial vehicle sensing solutions cannot meet their needs for long-range target detection. To address this challenge, this study proposes a long-range perception system for detecting road boundaries and trains based on millimeter-wave radar. The system uses high-resolution, long-range millimeter-wave radar customized for the strong scattering environment of rail transit. First, we established a multipath scattering theory in complex scenes such as track tunnels and fences and used the azimuth scattering characteristics to eliminate false detections. A set of accurate calculation methods of the train’s ego-velocity is proposed, which divides the radar detection point clouds into static target point clouds and dynamic target point clouds based on the ego-velocity of the train. We then used the road boundary curvature, global geometric parallel information, and multi-frame information fusion to extract and fit the boundary in the static target point stably. Finally, we performed clustering and shape estimation on the radar track information to identify the train and judge the collision risk based on the position and speed of the detected train and the extracted boundary information. The paper makes a significant contribution by establishing a multipath scattering theory for complex scenes of rail transit to eliminate radar false detection and proposing a train speed estimation strategy and a road boundary feature point extraction method that adapt to the rail environment. As well as building a perception system and installing it on the train for verification, the main line test results showed that the system can reliably detect the road boundary more than 400 m ahead of the train and can stably detect and track the train.

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False Detections Revising Algorithm for Millimeter Wave Radar SLAM in Tunnel

Cited by 7 publications

References 28 publications

Correlative Scan Matching Position Estimation Method by Fusing Visual and Radar Line Features

Correlative Scan Matching Position Estimation Method by Fusing Visual and Radar Line Features

Filtering Methods for Biomedical Image Denoising

Long-Range Perception System for Road Boundaries and Objects Detection in Trains

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