MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping
This work addresses the challenge of maintaining temporal consistency in high-definition maps for autonomous driving, representing an incremental advancement through the integration of tracking techniques and memory fusion.
The paper tackles the problem of inconsistent vector HD mapping over time by formulating it as a tracking task with memory fusion, resulting in performance improvements of over 8% on conventional metrics and 19% on new consistency-aware metrics on nuScenes and Agroverse2 datasets.
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 and models are available on our project page: https://map-tracker.github.io.