We describe a novel approach to image based localisation in urban
environments using semantic matching between images and a 2-D map. It contrasts
with the vast majority of existing approaches which use image to image database
matching. We use highly compact binary descriptors to represent semantic
features at locations, significantly increasing scalability compared with
existing methods and having the potential for greater invariance to variable
imaging conditions. The approach is also more akin to human map reading, making
it more suited to human-system interaction. The binary descriptors indicate the
presence or not of semantic features relating to buildings and road junctions
in discrete viewing directions. We use CNN classifiers to detect the features
in images and match descriptor estimates with a database of location tagged
descriptors derived from the 2-D map. In isolation, the descriptors are not
sufficiently discriminative, but when concatenated sequentially along a route,
their combination becomes highly distinctive and allows localisation even when
using non-perfect classifiers. Performance is further improved by taking into
account left or right turns over a route. Experimental results obtained using
Google StreetView and OpenStreetMap data show that the approach has
considerable potential, achieving localisation accuracy of around 85% using
routes corresponding to approximately 200 meters.

本文提出了一种新的基于图像的城市定位方法，使用图像与 2D 地图之间的语义匹配来表示语义特征，其中的二进制描述符能够更好地适应可变成像条件，而 CNN 分类器则用来检测图像的特征并与地图数据库中的信息进行匹配，这种方法相对于传统的图像数据库匹配更具有可扩展性和人机交互性.