The localization of self-driving cars is needed for several tasks such as keeping maps updated, tracking objects, and planning. Localization algorithms often take advantage of maps for estimating the car pose. Since maintaining and using several maps is computationally expensive, it is important to analyze which type of map is more adequate for each application. In this work, we provide data for such analysis by comparing the accuracy of a particle filter localization when using occupancy, reflectivity, color, or semantic grid maps. To the best of our knowledge, such evaluation is missing in the literature. For building semantic and colour grid maps, point clouds from a Light Detection and Ranging (LiDAR) sensor are fused with images captured by a front-facing camera. Semantic information is extracted from images with a deep neural network. Experiments are performed in varied environments, under diverse conditions of illumination and traffic. Results show that occupancy grid maps lead to more accurate localization, followed by reflectivity grid maps. In most scenarios, the localization with semantic grid maps kept the position tracking without catastrophic losses, but with errors from 2 to 3 times bigger than the previous. Colour grid maps led to inaccurate and unstable localization even using a robust metric, the entropy correlation coefficient, for comparing online data and the map.

中文翻译：

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自动驾驶汽车定位的最佳网格地图是什么？不同类型照明、交通和环境下的评估

自动驾驶汽车的本地化需要执行多项任务，例如保持地图更新、跟踪对象和规划。定位算法通常利用地图来估计汽车姿态。由于维护和使用多个地图的计算成本很高，因此分析哪种类型的地图更适合每个应用程序很重要。在这项工作中，我们通过比较使用占用率、反射率、颜色或语义网格图时粒子过滤器定位的准确性，为此类分析提供数据。据我们所知，文献中缺少这样的评估。为了构建语义和颜色网格图，来自光检测和测距 (LiDAR) 传感器的点云与前置摄像头捕获的图像融合。语义信息是通过深度神经网络从图像中提取的。实验在不同的环境、不同的光照和交通条件下进行。结果表明，占用网格图导致更准确的定位，其次是反射率网格图。在大多数情况下，使用语义网格图的定位保持位置跟踪没有灾难性损失，但误差比以前大 2 到 3 倍。即使使用稳健的指标熵相关系数来比较在线数据和地图，彩色网格地图也会导致定位不准确和不稳定。带有语义网格图的定位保持了位置跟踪，没有灾难性的损失，但误差比以前大 2 到 3 倍。即使使用稳健的指标熵相关系数来比较在线数据和地图，彩色网格地图也会导致定位不准确和不稳定。带有语义网格图的定位保持了位置跟踪，没有灾难性的损失，但误差比以前大 2 到 3 倍。即使使用稳健的指标熵相关系数来比较在线数据和地图，彩色网格地图也会导致定位不准确和不稳定。