利用三維點雲強度資訊與幾何模型之地標感測與車輛定位

Abstract

為了達成車輛全自主駕駛，車輛定位在自主駕駛車上是最基本的功能。雖然全球定位系統被廣泛的應用在車輛定位上，其定位仍然會受到一定量的偏差。為了減少定位偏差，行駛環境中的地標物可以用來輔助車輛定位。若此地標物的全球座標位置或是地標地圖在定位前是已知的，車輛的姿態與位置可以藉由計算偵測的地標與地圖上地標之間的轉換關係得知。在地標偵測演算法中，由光達所產生的三維點雲中能夠擷取出反射強度值以及幾何特徵。根據已知的地標物模型，地標物在車輛座標下的姿態與位置可由模型導向的方式估測。在初始估測完後並利用最佳化的方式降低模型配對的誤差。在車輛定位演算法中，偵測的地標物與地圖上的地標物會根據車輛預測的位置進行資料配對關係。當兩個以上的地標物被偵測且配對時，車輛在全球座標的位置可以透過地標物來估測。為了使估測的軌跡能夠更加平滑，卡爾曼濾波器中的時間與量測更新將使用於車輛定位演算法中。由實驗結果得知，利用所提出的演算法平均估測的偏差根據真實參考軌跡為0.19公尺。相較於只使用全球定位系統來定位的平均偏差1.81公尺還來的小。

In order to achieve full self-driving capability, localization is one of the basic function of future autonomous vehicle. Although GPS is widely used for localization, it suffers from bias generally. To reduce the bias, landmarks around the ego-vehicle can be used to enhance the localization performance. If the global position of landmarks (or the landmark map) are known, the vehicle pose can be estimated by finding the transformation between the detected landmarks and the landmarks in global coordinate. For landmark detection, intensity value and geometric feature are extracted from the 3-D point cloud captured by LiDAR. Based on the known model of landmark, a model-driven approach is used to estimate the pose of landmark in local coordinate. To reduce the model matching error, an optimization is performed after initial landmark pose estimation.For vehicle localization, the data association between the detected landmarks and the map are estimated based on the prediction vehicle pose. If two or more landmarks are available, vehicle pose can be estimated from the detected landmarks. In addition, to increase the smoothness of localization trajectory, Kalman filtering is used from both time update and measurement update. The experimental results show that the average localization bias of the proposed method with available ground truth could be reduced to 0.19m, which is lower than the bias of using GPS only (1.81m).