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[Radar SLAM] ― May 30, 2021

Radar Odometry Results on MulRan dataset


Radar

  • Here, the radar means Navtech radar data.
    • For details, see https://oxford-robotics-institute.github.io/radar-robotcar-dataset/
  • Radar is robust to occlusions than LiDAR in urban sites.

Yeti Radar Odometry

  • Paper
    • Do We Need to Compensate for Motion Distortion and Doppler Effects in Spinning Radar Navigation? (ICRA 2021)
  • Code
    • https://github.com/keenan-burnett/yeti_radar_odometry
    • Features
      • implemented cen2018, cen2019 method with motion compensation
    • Requirements
      • need ray-wise timestamps

Tutorial: running Yeti on MulRan dataset

  • see this video
    • link (TBA)

Results on MulRan dataset

  • MulRan dataset
    • MulRan: Multimodal Range Dataset for Urban Place Recognition (ICRA 2019)
    • https://sites.google.com/view/mulran-pr/dataset
  • Methods
    • Cen2018: Precise ego-motion estimation with millimeter-wave radar under diverse and challenging conditions (ICRA 2018)
    • Cen2019: Radar-only ego-motion estimation in difficult settings via graph matching (ICRA 2019)
  • Result captures 1 2
    • Left: Cen2018, Right: Cen2019
    • The trajectory color means start-blue and red-end.
KAIST 02


KAIST 03


DCC 01


DCC 02


DCC 03


Riverside 01


Riverside 02


Riverside 03


Sejong 02


Sejong 03



Comments

  1. ps. for the visualiation code, see https://github.com/gisbi-kim/yeti_odom_drawer 

  2. ps2. KAIST 01 and Sejong 01 sequences do not provide ray-wise timestamps, so the results are omitted.