Vision-based localization is a cost-effective and thus attractive solution
for many intelligent mobile platforms. However, its accuracy and especially
robustness still suffer from low illumination conditions, illumination changes,
and aggressive motion. Event-based cameras are bio-inspired visual sensors that
perform well in HDR conditions and have high temporal resolution, and thus
provide an interesting alternative in such challenging scenarios. While purely
event-based solutions currently do not yet produce satisfying mapping results,
the present work demonstrates the feasibility of purely event-based tracking if
an alternative sensor is permitted for mapping. The method relies on geometric
3D-2D registration of semi-dense maps and events, and achieves highly reliable
and accurate cross-modal tracking results. Practically relevant scenarios are
given by depth camera-supported tracking or map-based localization with a
semi-dense map prior created by a regular image-based visual SLAM or
structure-from-motion system. Conventional edge-based 3D-2D alignment is
extended by a novel polarity-aware registration that makes use of signed
time-surface maps (STSM) obtained from event streams. We furthermore introduce
a novel culling strategy for occluded points. Both modifications increase the
speed of the tracker and its robustness against occlusions or large view-point
variations. The approach is validated on many real datasets covering the
above-mentioned challenging conditions, and compared against similar solutions
realised with regular cameras.

使用基于事件的相机进行视觉定位，通过几何三维到二维的映射和极性感知的注册方法实现高可靠性和准确性的多模态跟踪，同时引入新的遮挡点筛选策略提高追踪器的速度和鲁棒性。