Building enterance recognition from street-level LiDAR and video images

每天，谷歌地图帮助世界各地城市的无数人找到他们正在寻找的建筑物，但一旦他们到达那里，他们通常会自己找到实际的入口进入。建筑物入口位置对于自动驾驶和地图搜索中的路由算法具有特殊意义，其结果可以将用户带到确切的入口。这是非常有用的，特别是对于大型建筑块，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入口信息。为了将入口信息添加到地图上，目前的做法是手工操作，这是一项劳动密集且繁琐的工作。本论文的研究内容是从街道级视频图像和LiDAR数据中自动识别建筑物入口。建筑物的入口在形状和颜色上有很大的不同。它们通常被行人和街道级数据中的其他物体部分遮挡，这使得自动识别成为一项非常具有挑战性的任务。该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，使用图形模型来制定联合概率。 候选检测、图像证据和场景信息。我们希望我们的入口识别方法将是通用的，足够强大的，各种各样的入口，在形状，颜色，纹理，遮挡，照明，尺度和观点。所提取的建筑物入口信息可以被添加到地图数据库中以提高地图精度， 从而增强地图搜索能力。最终，将使个人和最终自动汽车更容易找到前门。自动化过程还将显着提高地图生成的生产力。

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 candidate detection, image evidence and scene information. We expect that our entrance recognition method will be generic to, and robust enough for, a wide variety of entrances, in terms of shape, color, texture, occlusions, illumination, scales, and viewpoints. The extracted building entrance information can be added to a map database to improve map accuracy and thereby enhance map search capability. Eventually, will make it easier for individuals and eventually autonomous cars to find the front door. The automated process will also significantly improve productivity of map generation.