  In this paper, we propose a novel trajectory learning method that exploits
motion trajectories on topological map using recurrent neural network for
temporally consistent geolocalization of object. Inspired by human's ability to
both be aware of distance and direction of self-motion in navigation, our
trajectory learning method learns a pattern representation of trajectories
encoded as a sequence of distances and turning angles to assist
self-localization. We pose the learning process as a conditional sequence
prediction problem in which each output locates the object on a traversable
path in a map. Considering the prediction sequence ought to be topologically
connected in the graph-structured map, we adopt two different hypotheses
generation and elimination strategies to eliminate disconnected sequence
prediction. We demonstrate our approach on the KITTI stereo visual odometry
dataset which is a city-scale environment and can generate trajectory with
metric information. The key benefits of our approach to geolocalization are
that 1) we take advantage of powerful sequence modeling ability of recurrent
neural network and its robustness to noisy input, 2) only require a map in the
form of a graph and simply use an affordable sensor that generates motion
trajectory and 3) do not need initial position. The experiments show that the
motion trajectories can be learned by training an recurrent neural network, and
temporally consistent geolocation can be predicted with both of the proposed
strategies.
