We congratulate the winners Urban Robotics Lab. @ KAIST for Lidar Single-session, and Autonomous Systems Lab @ ETHZ for Lidar Multisession leaderboards. Additionally we congratulate XR Penguin for winning both vision only single + multi-session awards.

The cash award winners are

  • Lidar Single-session: Hyungtae Lim, Daebeom Kim, Gunhee Shin, Daehan Lee and Hyun Myung (Urban Robotics Lab @ KAIST): 3000 USD
  • Lidar Multi-session: Andrei Cramariuc (Autonomous Systems Lab @ ETHZ): 4000 USD
  • Vision only Single-session (Best academic submission): Hyunjun Lim and Hyun Myung (URL @ KAIST): 1000 USD
  • Vision only Multi-session: Andrei Cramariuc (Autonomous Systems Lab @ ETHZ): 2000 USD

Live Leaderboard

Vision/imu only leaderboard

Live Leaderboard

Vision/imu only leaderboard

Score Computation

After transformation of the estimates from the imu frame to the pole tip, we aligned the trajectory with the sparse ground truth points using a rigid transformation. Then the ATE for each point is computed (we rely on the evo script). Depending on the error, each ground truth point adds a certain amount of points to the score:

  • < 0.5cm → 20 points
  • < 1cm → 10 points
  • < 3cm → 6 points
  • < 6cm → 5 points
  • < 10cm → 3 point
  • < 40cm → 1 points
  • > 40cm → 0 points
In order to give each sequence the same weight, a normalization factor is introduced. Sequences for site 1 and 2 can score up to 100 points; while sequences for site 3 can score up to 200 points. That leads to a maximum of 1600 points.