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论文arXiv 2602.168701 个月前

Boreas road trip：面向挑战道路的多传感器自动驾驶数据集 ​

Boreas-RT扩展Boreas到多样挑战道路，用多传感器真实行驶数据评测里程计与度量定位算法。

AI 整理
  • 感知与导航
  • 数据与采集
  • 评测与安全

原文链接：Boreas road trip: A multi-sensor autonomous driving dataset on challenging roads（arXiv 2602.16870 · 发布日期：2026-09-07）

Boreas road trip: A multi-sensor autonomous driving dataset on challenging roads

Boreas-RT扩展Boreas到多样挑战道路，用多传感器真实行驶数据评测里程计与度量定位算法。

核心要点 ​

  • 现代自动驾驶算法在新地点和多样复杂道路条件下面临更具挑战的评测需求。
  • 数据包含60段9条真实路线共643公里，融合相机、雷达、激光雷达、IMU与轮编码器。
  • 基准显示多种先进里程计和定位算法在挑战路线显著退化，存在对简单环境过拟合。
  • 提供精确内外参、公开开发套件、里程计和度量定位实时排行榜，网站可获取。

验证范围与局限：验证范围为Boreas-RT的9条真实道路路线、60段序列及其多次通行条件。

英文摘要 (Abstract) ​

The Boreas Road Trip (Boreas-RT) dataset extends the multi-season Boreas dataset to new and diverse locations that pose challenges for modern autonomous driving algorithms. Boreas-RT comprises 60 sequences collected over 9 real-world routes, totalling 643 km of driving. Each route is traversed multiple times, enabling evaluation in identical environments under varying traffic and, in some cases, weather conditions. The data collection platform includes a 5MP FLIR Blackfly S camera, a 360° Navtech RAS6 Doppler-enabled spinning radar, a 128-channel 360° Velodyne Alpha Prime lidar, an Aeva Aeries II FMCW Doppler-enabled lidar, a Silicon Sensing DMU41 inertial measurement unit, and a Dynapar wheel encoder. Centimetre-level ground truth is provided via post-processed Applanix POS LV GNSS-INS data. The dataset includes precise extrinsic and intrinsic calibrations, a publicly available development kit, and a live leaderboard for odometry and metric localization. Benchmark results show that many state-of-the-art odometry and localization algorithms overfit to simple driving environments and degrade significantly on the more challenging Boreas-RT routes. Boreas-RT provides a unified dataset for evaluating multi-modal algorithms across diverse road conditions. The dataset, leaderboard, and development kit are available at boreas.utias.utoronto.ca.

作者：Daniil Lisus, Katya M. Papais, Cédric Le Gentil, Elliot Preston-Krebs, Andrew Lambert, Keith Y. K. Leung, Timothy D. Barfoot

来源

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  1. arXiv 2602.16870：Boreas road trip: A multi-sensor autonomous driving dataset on challenging roads访问 2026-10-21