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Boreas and Boreas-RT All-Weather Localization

Last updated: 2026-05-09

Boreas is a multi-season autonomous-driving dataset for evaluating localization and perception under repeated routes, seasonal change, rain, snow, night, and radar/LiDAR/camera variation. Boreas-RT extends the idea to more challenging routes and additional Doppler-capable sensors, making the pair one of the strongest public anchors for all-weather localization research.

Related pages: SLAM benchmarking metrics and datasets, radar odometry and radar SLAM, production LiDAR map localization


Scope

ItemBoreasBoreas-RT
Primary domainRepeated Toronto-area road route over 1 year9 real-world challenging routes
ScaleMore than 350 km, 44 sequences60 sequences, 643 km
ConditionsSun, cloud, rain, night, snow, seasonsRepeated routes with varying traffic and, on some routes, weather
Main benchmarkOdometry, metric localization, 3D object detectionOdometry and metric localization
Core questionRobust long-term localization across season/weatherGeneralization beyond simple repeated routes

Boreas is valuable because it combines repeated routes, radar, high-density LiDAR, camera, and centimeter-grade reference poses.


Sensors And Labels

AssetBoreas notes
LiDARVelodyne Alpha Prime 128-channel 360-degree LiDAR
RadarNavtech 360-degree scanning radar in Boreas; Boreas-RT uses a Doppler-enabled Navtech RAS6
Camera5 MP FLIR Blackfly S camera
Ground truthPost-processed Applanix POS LV GNSS/INS with centimeter-level accuracy
Additional Boreas-RT sensorsAeva Aeries II FMCW Doppler-enabled LiDAR, IMU, and wheel encoder
3D boxesBoreas provides 3D object labels for a sunny-weather subset
Devkitpyboreas supports data access, radar scans, pointcloud deskewing, Lie groups, and benchmark utilities

The original Boreas paper reports 7.1k annotated object frames and 320k boxes for Boreas-Objects-V1.


Tasks And Metrics

TaskPractical metric
OdometryKITTI-style translational drift percent and rotational drift over 100-800 m segments
Metric localizationAbsolute pose error, relocalization success, false accept rate, convergence time
Radar/LiDAR comparisonSame-sequence drift and localization availability by weather/season
3D object detectionPer-class mAP where sunny labeled subset is used
Cross-route generalizationBoreas-RT route-level degradation and failure case rate

For airside-style release decisions, report per-condition and per-route results rather than only a leaderboard mean.


Best Use

Use Boreas and Boreas-RT to:

  • compare radar, LiDAR, and camera localization under weather and season change;
  • stress map aging across repeated routes;
  • evaluate long-route odometry drift with centimeter-grade reference poses;
  • validate metric localization leaderboards before private map-release tests;
  • test whether methods overfit to simple urban loops and fail on harder routes.

Boreas is one of the strongest public datasets for radar-vs-LiDAR localization tradeoffs in adverse weather.


Airside Transfer

Airports need localization that survives rain, snow, night, wet pavement, repeated geometry, and map aging. Boreas can shape:

  • public B1 localization benchmark tiers before airport replay;
  • radar fallback evaluation when LiDAR/camera degrade;
  • map-age and route-repeat split design;
  • false relocalization analysis on repeated structures.

Airside validation still needs private airport routes. Boreas road scenes lack open aprons, terminal overhang GNSS multipath, stand/gate aliasing, aircraft/GSE occlusion, cones/chocks, and operational no-go zones.


Limitations

  • Boreas is road-domain and not an airport map benchmark.
  • 3D object labels are not available for all conditions; the labeled subset is sunny-weather focused.
  • Spinning radar is excellent for localization but differs from sparse automotive 4D radar point-cloud detection.
  • Strong GNSS/INS ground truth does not remove the need to validate GNSS-denied or multipath airport zones.
  • Boreas-RT is a newer 2026 preprint-era extension; pin release versions for reproducible comparisons.

Sources

Public research notes collected from public sources.