Road Boundaries Detection based on Modified Occupancy Grid Map Using Millimeter-wave Radar

Xu, Fenglei; Wang, Huan; Hu, Bingwen; Ren, Mengye

doi:10.1007/s11036-019-01378-5

Cited by 16 publications

(6 citation statements)

References 14 publications

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“…Only collision-free paths are safe and acceptable; all the generated paths in the path candidates should be checked. We use a binary occupancy grid map 26 to establish the environment model.…”

Section: Collision Checkingmentioning

confidence: 99%

An event-triggered real-time motion planning strategy for autonomous vehicles

Chen

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Motion planning is an essential part of autonomous vehicles. The planning process should respond to environmental changes in real time to ensure safety. This paper proposes an event-triggered real-time motion planning strategy to achieve a more real-time planning effect and a scenario-based planning process. The path planning process is discretized into several parts and integrated into the behavioral planning process. A hierarchical finite state machine (HFSM) based integrated motion planning process is proposed to trigger the discrete path planning parts according to environmental events. Thus, the output reference path can be obtained. Experiment and simulation results show the efficiency of our strategy.

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Section: Collision Checkingmentioning

confidence: 99%

An event-triggered real-time motion planning strategy for autonomous vehicles

Chen

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…When DBSCAN is used to filter outliers from radar data, the two-dimensional position features r m x and r m y of targets are usually used, which are the position of targets observed by radar, as described by the authors of [ 7 , 8 ]. When the DBSCAN processing radar uses data with two-dimensional position features, it essentially divides point clouds according to Euclidean distance between targets.…”

Section: Radar Data Preprocessingmentioning

confidence: 99%

“…Based on the above advantages, this paper uses DBSCAN to cluster millimeter-wave radar data, and then selects the target point cloud body in the clustering result to achieve the filtering of outliers. When using DBSCAN to cluster millimeter-wave radar data, usually only the two-dimensional Cartesian position features of the target are used, such as those described by the authors of [ 7 , 8 ], but due to the unevenness of the millimeter-wave radar data density, relying on the location features sometimes cannot accurately identify outliers. The authors of [ 9 ] used the speed characteristics of the target, but in a static environment, the target speed was 0, and only the position characteristics were actually used.…”

Section: Introductionmentioning

confidence: 99%

The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU

Liu

Wang

et al. 2020

Sensors

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Compared with the commonly used lidar and visual sensors, the millimeter-wave radar has all-day and all-weather performance advantages and more stable performance in the face of different scenarios. However, using the millimeter-wave radar as the Simultaneous Localization and Mapping (SLAM) sensor is also associated with other problems, such as small data volume, more outliers, and low precision, which reduce the accuracy of SLAM localization and mapping. This paper proposes a millimeter-wave radar SLAM assisted by the Radar Cross Section (RCS) feature of the target and Inertial Measurement Unit (IMU). Using IMU to combine continuous radar scanning point clouds into “Multi-scan,” the problem of small data volume is solved. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is used to filter outliers from radar data. In the clustering, the RCS feature of the target is considered, and the Mahalanobis distance is used to measure the similarity of the radar data. At the same time, in order to alleviate the problem of the lower accuracy of SLAM positioning due to the low precision of millimeter-wave radar data, an improved Correlative Scan Matching (CSM) method is proposed in this paper, which matches the radar point cloud with the local submap of the global grid map. It is a “Scan to Map” point cloud matching method, which achieves the tight coupling of localization and mapping. In this paper, three groups of actual data are collected to verify the proposed method in part and in general. Based on the comparison of the experimental results, it is proved that the proposed millimeter-wave radar SLAM assisted by the RCS feature of the target and IMU has better accuracy and robustness in the face of different scenarios.

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“…However, in practical applications, the localization results are inadequate, the matching time is long, and the iterative process may fall into a local optimum. (14) To develop a method that can be adapted to accurate localization indoors, in this paper, we propose a method that uses an occupancy grid map (15) to represent an arbitrary position of an autonomous mobile platform and performs a map gradient approximation. The method analyzes the effect of the multi-resolution map to perform a series of 3D pose estimation tasks.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Indoor Localization Technique Based on Point Cloud Conversion Image Matching

Zhao¹,

He²,

Wang³

et al. 2023

Sensors and Materials

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It is important that indoor autonomous mobile platforms have the capability of localization in general indoor environments. In this study, using multi-threaded vehicle-mounted light detection and ranging (LiDAR), we conducted indoor autonomous mobile platform localization experiments based on a point cloud conversion 2D image method with an interpolated probability distribution, performed a scan matching analysis by converting 2D images based on an interpolated probability distribution while using occupied grid maps, and introduced a multiresolution map method to avoid falling into a local optimum. We found that the method adopted in this study achieves a higher indoor positioning accuracy and a higher matching speed with reduced computational effort while avoiding local optima. Compared with other traditional indoor positioning methods, this method has the advantages of universal applicability and robustness against signal interference and other problems.

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Road Boundaries Detection based on Modified Occupancy Grid Map Using Millimeter-wave Radar

Cited by 16 publications

References 14 publications

An event-triggered real-time motion planning strategy for autonomous vehicles

An event-triggered real-time motion planning strategy for autonomous vehicles

The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU

Enhanced Indoor Localization Technique Based on Point Cloud Conversion Image Matching

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