  Deep reinforcement learning (DRL) demonstrates great potential in mapless
navigation domain. However, such a navigation model is normally restricted to a
fixed configuration of the range sensor because its input format is fixed. In
this paper, we propose a DRL model that can address range data obtained from
different range sensors with different installation positions. Our model first
extracts the goal-directed features from each obstacle point. Subsequently, it
chooses global obstacle features from all point-feature candidates and uses
these features for the final decision. As only a few points are used to support
the final decision, we refer to these points as support points and our approach
as support point-based navigation (SPN). Our model can handle data from
different LiDAR setups and demonstrates good performance in simulation and
real-world experiments. Moreover, it shows great potential in crowded scenarios
with small obstacles when using a high-resolution LiDAR.
