MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping
Jiacheng Chen, Yuefan Wu, Jiaqi Tan, Hang Ma, Yasutaka Furukawa

TL;DR
MapTracker introduces a novel vector HD-mapping algorithm that formulates mapping as a tracking problem, leveraging memory buffers of raster and vector latents to ensure consistent, temporally aligned reconstructions of road elements over time.
Contribution
The paper proposes MapTracker, a new method that fuses strided memory latents for improved temporal consistency in HD-mapping, with benchmark improvements on nuScenes and Agroverse2 datasets.
Findings
Outperforms existing methods by over 8% on nuScenes
Achieves over 19% improvement on Agroverse2 with consistency metrics
Provides enhanced processing code and augmented metrics for better benchmarking
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…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Robotics and Sensor-Based Localization · Advanced Vision and Imaging
