MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping

ECCV 2024 (Oral)

Abstract

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.

Video

Example reconstruction results

Comparisons with baselines

More merged global reconstruction results

nuScenes dataset



Agroverse2 dataset

BibTeX

@article{chen2024maptracker,
  author    = {Chen, Jiacheng and Wu, Yuefan and Tan, Jiaqi and Ma, Hang and Furukawa, Yasutaka},
  title     = {MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping},
  journal   = {arXiv preprint arXiv:2403.15951},
  year      = {2024},
}