Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD maps is highly relevant for the current advances in autonomous driving, but remains a challenging topic in research. We present a novel monocular localization approach based on a sliding-window pose graph that leverages predicted uncertainties for increased precision and robustness against challenging scenarios and per frame failures. To this end, we propose an efficient multi-task uncertainty-aware perception module, which covers semantic segmentation, as well as bounding box detection, to enable the localization of vehicles in sparse maps, containing only lane borders and traffic lights. Further, we design differentiable cost maps that are directly generated from the estimated uncertainties. This opens up the possibility to minimize the reprojection loss of amorphous map elements in an association free and uncertainty-aware manner. Extensive evaluation on the Lyft 5 dataset shows that, despite the sparsity of the map, our approach enables robust and accurate 6D localization in challenging urban scenarios
翻译:使用低成本传感器设置和零散的HD地图在密集的城市假设情景中扎实定位,对于目前自主驾驶的进展非常重要,但仍然是一项具有挑战性的研究课题。我们根据滑动窗形图展示了一种新型的单方本地化方法,该方法利用预测的不确定性来提高精确性和稳健性,以应对具有挑战性的假设情景和每个框架的失败。为此,我们提议了一个高效的多任务不确定感知模块,其中包括语义分解以及捆绑式箱检测,以便能够在稀少的地图中实现车辆本地化,只包含车道边界和交通灯。此外,我们设计了由估计的不确定性直接生成的可区分成本地图。这为以自由、有不确定性的方式尽量减少不固定的地图元素的再预测损失开辟了可能性。对Lyft 5数据集的广泛评估表明,尽管地图十分简洁,但我们的方法使得具有挑战性的城市假设情景的6D本地化得以实现可靠和准确的6D本地化。