MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD
  Mapping
Jiacheng Chen Yuefan Wu Jiaqi Tan Hang Ma Yasutaka Furukawa
This paper presents a vector HD-mapping algorithm that formulates the mapping
as a tracking task and uses a history of memory latents to ensure consistent
reconstructions over time. Our method, MapTracker, accumulates a sensor stream
into memory buffers of two latent representations: 1) Raster latents in the
bird's-eye-view (BEV) space and 2) Vector latents over the road elements (i.e.,
pedestrian-crossings, lane-dividers, and road-boundaries). The approach borrows
the query propagation paradigm from the tracking literature that explicitly
associates tracked road elements from the previous frame to the current, while
fusing a subset of memory latents selected with distance strides to further
enhance temporal consistency. A vector latent is decoded to reconstruct the
geometry of a road element. The paper further makes benchmark contributions by
1) Improving processing code for existing datasets to produce consistent ground
truth with temporal alignments and 2) Augmenting existing mAP metrics with
consistency checks. MapTracker significantly outperforms existing methods on
both nuScenes and Agroverse2 datasets by over 8% and 19% on the conventional
and the new consistency-aware metrics, respectively. The code will be available
on our project page: https://map-tracker.github.io.