Iterative Closest Point Method for Point Cloud Data Processing of Millimeter Wave Radar

‍ ‍As an all weather and all day sensing tool， millimeter-wave radar with the characteristic of miniaturized size and low cost plays an important role in advanced assisted driving systems. The multiple-input multiple-out （MIMO） technology based on multi-chip cascade scheme can effectively improve the angular resolution of the millimeter-wave radar， making it possible to provide point cloud images of scene. To handle the drawbacks of sparse point cloud and serious noise in millimeter-wave radar image， this paper proposes a novel algorithm of multi-frame fusion based on iterative closest point （ICP） processing and density-based spatial clustering of applications with noise （DBSCAN） which has adaptive neighborhood radius. First， MIMO millimeter-wave radar technology is employed to obtain the point cloud image of the target in the observation scene. Secondly， the odometry information is used to obtain the initial value of point cloud matching， and then the iterative closest point algorithm is used to estimate the translation and rotation matrix to achieve accurate matching. In this way， the problem of sparse point cloud is effectively solved through multi-frame data fusion. Then， the noise is removed by the adaptive threshold DBSCAN algorithm to obtain the point cluster information of the target. The minimum circumscribed rectangle is obtained for the clustered point cluster targets， which enables distinguishing between different types of targets such as cars and fences by combining the scattering intensity of the target. Finally， the validity of the algorithm proposed in this paper is verified by the test data in the field （a typical parking lot scene）.