Building enterance recognition from street-level LiDAR and video images

每天，谷歌地图都会帮助世界各地城市的无数人找到他们正在寻找的建筑，但一旦他们到达那里，他们通常要靠自己找到进入的实际入口。建筑物入口位置对于自动驾驶和地图搜索中的路线算法具有特殊的意义，在地图搜索中，结果可以将用户带到准确的入口。这是非常有用的，特别是对于大型构建块，其中由于地图中缺乏入口位置信息，当前的路由算法无法提供准确的条目信息。将入口信息添加到地图上，目前的做法是人工操作，这是一项劳动强度大、繁琐的任务。本文的研究内容是利用街道视频图像和激光雷达数据对建筑物入口进行自动识别。建筑物入口在形状和颜色方面差别很大。它们经常被来自街道水平数据的行人和其他物体部分遮挡，这使得自动识别成为一项非常具有挑战性的任务。提出的研究将开发一种新的传感器融合方法用于建筑物入口检测，该方法利用视频图像和LiDAR的优势，使用图形模型来表达

Every day, Google Maps helps countless people in cities around the world find buildings they are looking for, but once they get there, they are usually on their own to find the actual entrance to get in. Building entrance locations have particular meaning for autonomous driving, and routing algorithms in map searches where the results can bring users to the exact entry. This is quite useful, especially for a large building block, where current routing algorithms cannot provide accurate entry information due to the lack of entrance location information in the maps. To add entrance information to the maps, manual operation is current practice which is a labor intensive and tedious task. The proposed research is automatic recognition of building entrance from street-level video images and LiDAR data. Building entrances vary greatly in terms of shape and color. They are often partially occluded by pedestrians and other objects from street level data, which makes automatic recognition a very challenging task. The proposed research will develop a novel sensor fusion approach for building entrance detection that takes advantages of video images and LiDAR, which use a graphical model to formulate the joint probability of the