One model, one set of weights, for the IROS 2026 TartanIMU cross-platform inertial odometry challenge. The network predicts body-frame velocity from 6-axis IMU alone for car, drone, quadruped and handheld motion; there are no per-platform experts and no model switching at inference. An all-zero submission scores exactly 1.000. The best public score is ≈ 0.20074, so this The checkpoint is self-contained: it stores the per-channel mean/std used for input standardization plus all architecture hyper-parameters, so inference needs no extra flags or training-time artefacts. --stride 5 and --tta-shifts 0,25,-25 are the inference configuration of the submitted system (see compliance item 3). Using…
Independent publisher
Xuandong Liang
xunden
Models in Library2
Datasets in Library0
Models on Hugging Face5
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Published tasks: Robotics.
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Models
This repository is the second of the two single-model checkpoints that team Xunden selected on the Kaggle leaderboard. The first one lives at (RICH2200tv, 31-channel inputs, 200 epochs). The two checkpoints are independent single models: each generated its own selected CSV on its own, and no prediction in either CSV is an ensemble, blend or average of the two. The official per-sequence scoring service was not run on this particular CSV; its recorded result is the Kaggle public leaderboard entry above. The richer companion model in the sister repository reports the official per-sequence service numbers. The checkpoint is self-contained: it stores per-channel mean/std for input…