  In self-driving, standalone GPS is generally considered to have insufficient
positioning accuracy to stay in lane. Instead, many turn to LIDAR localization,
but this comes at the expense of building LIDAR maps that can be costly to
maintain. Another possibility is to use semantic cues such as lane lines and
traffic lights to achieve localization, but these are usually not continuously
visible. This issue can be remedied by combining semantic cues with GPS to fill
in the gaps. However, due to elapsed time between mapping and localization, the
live GPS frame can be offset from the semantic map frame, requiring
calibration. In this paper, we propose a robust semantic localization algorithm
that self-calibrates for the offset between the live GPS and semantic map
frames by exploiting common semantic cues, including traffic lights and lane
markings. We formulate the problem using a modified Iterated Extended Kalman
Filter, which incorporates GPS and camera images for semantic cue detection via
Convolutional Neural Networks. Experimental results show that our proposed
algorithm achieves decimetre-level accuracy comparable to typical LIDAR
localization performance and is robust against sparse semantic features and
frequent GPS dropouts.
