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

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.

AutoPlace: Robust Place Recognition with Single-chip Automotive Radar

“…RCS is a property of the object's reflectivity, mainly determined by the object's material, size and reflected angle. This measurement provides an additional feature for an object and has proven effective in assisting odometry task [32], [33]. However, the question remains whether and how the RCS can contribute to a place recognition task.…”

“…Since a single radar point cloud is too sparse to extract useful information due to the low sensing quality, we follow typical data pre-processing of [19], [33] and concatenate the nearest seven radar point clouds to form a denser point cloud. Besides, as the far-range measurement is less accurate, we crop the radar measurements to retain the points within 100 m from the sensor.…”

X-Band Radar Cross-Section of Tandem Helicopter Based on Dynamic Analysis Approach

“…The difference in structure from conventional unmanned helicopters is that unmanned tandem helicopters have no slender tail boom, with two rotors of the same size arranged in the longitudinal direction, one in the front and one in the back, and the rotors have the same rotation speed and opposite rotation directions [ 21 , 22 , 23 ]. In order to ensure the accuracy of simultaneous localization and mapping (SLAM) in the millimeter-wave band [ 24 , 25 ], a millimeter-wave radar SLAM assisted by the RCS feature of the target and inertial measurement unit is presented. With the extensive equipment of dual-band radars on various weapon platforms, the study of the electromagnetic scattering characteristics of helicopters in the X-band has also become important.…”

RTOB SLAM: Real-Time Onboard Laser-Based Localization and Mapping

“…Over the last couple of years, ground and air autonomous vehicles have become incredibly popular in academia and industry. For position estimation, most of them rely completely on GPS and their inertial sensors, som even millimeter radar waves [1]. These sensors give an estimate of the position but do not provide any information on the environment, and have limited accuracy -in particular for GPS.…”

“…performing the SLAM task in real-time with limited computational resources [6], different approaches can be found in literature. They can broadly be divided in two groups: (1) One group tries to simplify the SLAM problem by making assumptions about the environment. (2) The other group tries to simplify or speed up the algorithm while relaxing the assumptions on the environment.…”