Autonomous navigation emerges from both motion and local visual perception in
real-world environments. However, most successful robotic motion estimation
methods (e.g. VO, SLAM, SfM) and vision systems (e.g. CNN, visual place
recognition-VPR) are often separately used for mapping and localization tasks.
Conversely, recent reinforcement learning (RL) based methods for visual
navigation rely on the quality of GPS data reception, which may not be reliable
when directly using it as ground truth across multiple, month-spaced traversals
in large environments. In this paper, we propose a novel motion and visual
perception approach, dubbed MVP, that unifies these two sensor modalities for
large-scale, target-driven navigation tasks. Our MVP-based method can learn
faster, and is more accurate and robust to both extreme environmental changes
and poor GPS data than corresponding vision-only navigation methods. MVP
temporally incorporates compact image representations, obtained using VPR, with
optimized motion estimation data, including but not limited to those from VO or
optimized radar odometry (RO), to efficiently learn self-supervised navigation
policies via RL. We evaluate our method on two large real-world datasets,
Oxford Robotcar and Nordland Railway, over a range of weather (e.g. overcast,
night, snow, sun, rain, clouds) and seasonal (e.g. winter, spring, fall,
summer) conditions using the new CityLearn framework; an interactive
environment for efficiently training navigation agents. Our experimental
results, on traversals of the Oxford RobotCar dataset with no GPS data, show
that MVP can achieve 53% and 93% navigation success rate using VO and RO,
respectively, compared to 7% for a vision-only method. We additionally report a
trade-off between the RL success rate and the motion estimation precision.

提出了 MVP 方法，该方法利用视觉感知和优化运动估计数据相结合，通过强化学习实现无人驾驶车辆的自主导航，相较于传统的仅利用视觉进行导航的方法，MVP 在大尺度环境下具有更高的学习速度、准确性和稳健性，同时克服了 GPS 数据的可靠性问题。