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.
By Luca Gandolfi, Simone Nascivera, Roberto Pellerito, Rong Zou, Chiara Plizzari, Davide Scaramuzza
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.
By Chankyo Kim, Minghan Zhu, Tzu-Yuan Lin, Avantika Rattan, Maani Ghaffari
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...
By Bingqi Huang, Bingchuan Wei, Yingkai Cai, Zhaokui Wang
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...
By Yuhan Wang, Yurou Chen, Hongye Jiang, Wenzhao Lian
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.
By Simone Nascivera, Leonard Bauersfeld, Jeff Delaune, Davide Scaramuzza
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...
By Quentin Herau, Nathan Piasco, Moussab Bennehar, Luis Rold\~ao, Dzmitry Tsishkou, Bingbing Liu, Cyrille Migniot, Pascal Vasseur, C\'edric Demonceaux
arXiv:2609.21597v1 Announce Type: new
Abstract: Monocular 6-DoF pose estimation of non-cooperative targets is important for on-orbit servicing and debris removal. A single-image estimator can confuse...
By Andr\'e Lopo, Atabak Dehban, Rodrigo Ventura
LPA-CWM introduces a Learned Physical Adjudicator (LPA) to improve counterfactual world models (CWM) for motion reasoning by learning to weight candidate responses based on visual context and response structure. The 3.0M‑parameter LPA is trained on dense MOVi‑F trajectories while keeping the CWM predictor and intervention generator frozen. A new Completeness‑aware Motion Correspondence (CMC) protocol evaluates localization, trajectory completeness, visibility, and continuity, and LPA‑CWM achieves significant gains on DAVIS and Kinetics subsets.
By Kunwei Wu, Xiang Liu, Guocai Yao, Junming Chen, Zhikang Chen, Min Zhang, Pengwei Wang, Sen Cui
Deep-learning features excel in visual matching, yet their practical value in tightly coupled visual-inertial SLAM (VI-SLAM) remains insufficiently characterized. We present DL-VINS-Factory, a unified framework that integrates learned feature extractors (ALIKED, RaCo, SuperPoint, XFeat) with either Lucas--Kanade (LK) optical-flow tracking or LightGlue (LG) descriptor matching.
arXiv:2509.04600v2 Announce Type: replace
Abstract: Reconstructing global human motion from monocular video is fundamental to VR, graphics, and robotics, yet remains ill-posed due to depth ambiguity,...
By Zhongyuan Hu, Qijun Ying, Jiazhi Shu, Ronghui Li, Yu Lu, Zijiao Zeng, Xiu Li
arXiv:2609.07534v1 Announce Type: cross
Abstract: Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sustained c...
By Yuhan Wang, Yurou Chen, Hongye Jiang, Wenzhao Lian
arXiv:2608.20891v1 Announce Type: cross
Abstract: We present a vision-only state estimation system for X-configuration quadcopters equipped with a canonical stereo camera pair and no inertial sensors...
By Daniel Gr{\o}nhaug, Sofie Markeset, Mathias Kolberg