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arXiv Computer Vision By Simone Nascivera, Leonard Bauersfeld, Jeff Delaune, Davide Scaramuzza

Online Adaptation of Visual Odometry Frontends with Image-Conditioned Reinforcement Learning

Read the original on arXiv Computer Vision →

The paper introduces a visual odometry frontend that automatically and continuously adapts its parameters using an image-conditioned reinforcement learning policy. The policy selects key tuning values—FAST detection threshold, KLT patch size, and RANSAC rejection threshold—based on a lightweight image embedding and frontend statistics, with a privileged critic aiding training. Trained on synthetic data, the approach transfers zero‑shot to real-world benchmarks, improving the tracking‑computation trade‑off by up to 8% in accuracy and 57% in runtime compared to static configurations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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