Abstract
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.
Highlights
In recent years, with the popularity of robots, drones, unmanned driving, and Virtual Reality/Augmented Reality (VR/AR), Simultaneous Localization and Mapping (SLAM) technology has become well-known and is considered to be one of the key technologies in these fields
Technology was first proposed in the field of robots. It refers to robots which start from unknown locations in unknown environments, locate their positions and postures through repetitive observations of environmental features during their movements, and construct an incremental map of the surrounding environment according to their own positions, so as to achieve the purpose of simultaneous localization and mapping
Because the millimeter-wave radar SLAM proposed in this paper eliminated low-precision points and outliers in the data preprocessing step, it greatly reduced the amount of calculation and storage of the computer
Summary
With the popularity of robots, drones, unmanned driving, and Virtual Reality/. DBSCAN to cluster millimeter-wave radar data, and selects the target point cloud body in the clustering result to achieve the filtering of outliers. In order to solve the above problems, this paper introduces the RCS feature of the target when using DBSCAN to improve the accuracy of identifying outliers and achieve the adaptive determination of parameters. The authors of [25] proposed a new point association technique to match the sparse measurements of the low-cost millimeter-wave radar. The point cloud matching method has been successfully used in radar self-motion estimation [34] and location [35], it is rarely used in SLAM based on the millimeter-wave radar. By the Radar Cross Section (RCS) features of the target and Inertial measurement unit (IMU)
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