RTMap: Real-Time Recursive Mapping with Change Detection and Localization

• URL: http://arxiv.org/abs/2507.00980v2
• Date: Wed, 30 Jul 2025 02:43:16 GMT
• Title: RTMap: Real-Time Recursive Mapping with Change Detection and Localization
• Authors: Yuheng Du, Sheng Yang, Lingxuan Wang, Zhenghua Hou, Chengying Cai, Zhitao Tan, Mingxia Chen, Shi-Sheng Huang, Qiang Li,
• Abstract summary: RTMap persistently crowdsourcing a multi-traversal HD map as a self-evolutional memory.<n>On onboard agents, RTMap simultaneously addresses three core challenges in an end-to-end fashion.<n> Experiments on several public autonomous driving datasets demonstrate our solid performance on both the prior-aided map quality and the localization accuracy.
• Score: 8.343318095882232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While recent online HD mapping methods relieve burdened offline pipelines and solve map freshness, they remain limited by perceptual inaccuracies, occlusion in dense traffic, and an inability to fuse multi-agent observations. We propose RTMap to enhance these single-traversal methods by persistently crowdsourcing a multi-traversal HD map as a self-evolutional memory. On onboard agents, RTMap simultaneously addresses three core challenges in an end-to-end fashion: (1) Uncertainty-aware positional modeling for HD map elements, (2) probabilistic-aware localization w.r.t. the crowdsourced prior-map, and (3) real-time detection for possible road structural changes. Experiments on several public autonomous driving datasets demonstrate our solid performance on both the prior-aided map quality and the localization accuracy, demonstrating our effectiveness of robustly serving downstream prediction and planning modules while gradually improving the accuracy and freshness of the crowdsourced prior-map asynchronously. Our source-code will be made publicly available at https://github.com/CN-ADLab/RTMap.

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