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DAPEVO is a learned visual odometry system that independently estimates image and event correspondences at shared patch locations and fuses their correlation evidence before motion refinement. It maintains image and event descriptors for each tracked patch, using a learned scalar gate to combine modality-specific correlation embeddings for each patch–frame edge, followed by a shared recurrent refinement and bundle‑adjustment update. The method supports event‑only observations and modality‑aware keyframe culling, achieving low trajectory error even when RGB frames are sparse or degraded, outperforming DPVO, RAMP‑VO, and event‑only DEVO on UZH‑FPV and TartanEvent datasets.
GINIO is a geometric SO(3)-equivariant interface designed for neural inertial odometry that ensures learned measurements transform consistently under any IMU mounting convention. It predicts motion measurements and uncertainties that obey vector and tensor transformation laws, and introduces Last-Frame Alignment to enable efficient sensor-frame learning equivalent to world-frame training. The interface is instantiated in several architectures—filter-connected NIO, AirIO-style recurrent aerial prediction, EqNIO-style full-SO(3) canonicalization, and ResNet-style temporal backbones—achieving significant accuracy and efficiency gains across multiple benchmarks.
arXiv:2608.21402v1 Announce Type: cross Abstract: World action models (WAMs) jointly denoise future video frames and robot actions, and the video prior is expected to generalize their control. Camera...
arXiv:2609.07534v2 Announce Type: replace-cross Abstract: Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sus...
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.
arXiv:2504.15776v2 Announce Type: replace Abstract: Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccur...