In self-driving, standalone GPS is generally considered to have insufficient positioning accuracy to stay in lane. Instead, many turn to LIDAR localization, but this comes at the expense of building LIDAR maps that can be costly to maintain. Another possibility is to use semantic cues such as lane lines and traffic lights to achieve localization, but these are usually not continuously visible. This issue can be remedied by combining semantic cues with GPS to fill in the gaps. However, due to elapsed time between mapping and localization, the live GPS frame can be offset from the semantic map frame, requiring calibration. In this paper, we propose a robust semantic localization algorithm that self-calibrates for the offset between the live GPS and semantic map frames by exploiting common semantic cues, including traffic lights and lane markings. We formulate the problem using a modified Iterated Extended Kalman Filter, which incorporates GPS and camera images for semantic cue detection via Convolutional Neural Networks. Experimental results show that our proposed algorithm achieves decimetre-level accuracy comparable to typical LIDAR localization performance and is robust against sparse semantic features and frequent GPS dropouts.
翻译:在自行驾驶过程中,一般认为独立全球定位系统的定位准确性不足以停留在车道上。 相反,许多人转而使用LIDAR本地化,但这样做的代价是建造LIDAR地图,因为维护成本高昂。另一种可能性是使用线路线和交通灯等语义提示,以实现本地化,但这些信号通常不会持续可见。这个问题可以通过将语义提示与全球定位系统相结合以填补空白来加以解决。然而,由于绘图与本地化之间时间过长,现场全球定位系统框架可以从语义地图框架中抵消,需要校准。在本文件中,我们提出一种强大的语义本地化算法,通过利用通用语义提示,包括交通灯和车道标志,在现场定位和语义地图框架之间进行自我校准,以抵消现场全球定位系统和语义图框架之间的偏差。我们用一个经过修改的意大利语系扩展的扩展卡尔曼过滤器来解决这个问题,它包含全球定位系统和相机图像,通过Convolual Neuration网络进行语义提示检测。实验结果显示,我们提议的算法实现了与典型的LIDAR本地性强度和静态静态性静态等特征。