The paper introduces CalfVO, a monocular visual odometry system that operates without camera intrinsics, test‑time optimization, bundle adjustment, or loop closure. Using a transformer, it predicts relative poses with separate rotation and translation confidences over overlapping image windows, then aggregates these predictions via a confidence‑weighted module to produce a single trajectory. CalfVO achieves the highest accuracy among calibration‑free methods across five benchmarks and runs at 53 FPS, outperforming all baselines.
By Vladimir Yugay, Duy-Kien Nguyen, Theo Gevers, Cees G. M. Snoek, Martin R. Oswald
arXiv:2609.13733v1 Announce Type: new
Abstract: Stable and reliable 4D spatial understanding is fundamental for autonomous driving systems. While feedforward reconstruction networks can estimate came...
By Meng-Li Shih, Shih-Yang Su, Yuliang Zou, Hao Xiang, Haidong Zhu, Vincent Casser, Brian Curless, Dmitry Kalenichenko, Mingxing Tan, Dragomir Anguelov
MDE-VIO integrates learned depth priors into the VINS-Mono optimization backend to improve visual‑inertial odometry in low‑texture environments. The framework enforces affine‑invariant depth consistency and pairwise ordinal constraints while filtering unstable artifacts with variance‑based gating, keeping computation within edge‑device limits. Experiments on TartanGround and M3ED datasets show the method prevents divergence and reduces Absolute Trajectory Error by up to 28.3%.
By Arda Alniak, Sinan Kalkan, Mustafa Mert Ankarali, Afsar Saranli, Abdullah Aydin Alatan
arXiv:2609.06074v2 Announce Type: replace
Abstract: Sparse optical flow provides stable inter-frame correspondence, playing a key role in Visual Odometry (VO) and Visual-Inertial Odometry (VIO). Clas...
By Yicheng Lin, Zhipeng Fei, Yuxiu Xu, WenDong Chen, Cong Li, Bin Han
arXiv:2608.24544v1 Announce Type: new
Abstract: Many feature-based visual-inertial odometry (VIO) systems rely on sparse feature tracking, whose accuracy and robustness directly affect state estimati...
By Renbiao Jin, Danping Zou, Wenxian Yu
arXiv:2405.07392v4 Announce Type: replace-cross
Abstract: Many existing visual SLAM methods can achieve high localization accuracy in dynamic environments by leveraging deep learning to mask moving o...
By Yuhao Zhang, Mihai Bujanca, Mikel Luj\'an
The paper "End-to-End Visual Odometry with RNNs and Attention" presents a study of deep‑learning approaches to visual odometry (VO), proposing a novel temporal attention‑based model to enhance performance. It evaluates existing end‑to‑end VO methods and explores their effectiveness on hand‑held camera data, contrasting with the typical driving‑scene training sets. The work aims to improve VO accuracy in more dynamic and complex visual environments.
By Ruiyu Li, Yinjia Liu, Alexander Yu
arXiv:2603. 25937v2 Announce Type: replace-cross Abstract: Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations.
By Maeva Guerrier, Karthik Soma, Jana Pavlasek, Giovanni Beltrame
HexVIO is a visual‑inertial odometry system that offloads the stereo‑inertial visual frontend to the Hexagon DSP co‑processor while keeping the backend on the main CPU. By optimizing for the DSP architecture, it achieves a 67% reduction in power consumption or an 86% increase in throughput on a commodity smartphone, enabling sustained 30 fps tracking at only 0.83 W. This allows for long‑term, all‑day real‑time tracking—approximately 18 hours on the tested device—on low‑power mobile and XR hardware.
By Patrick Wolf, Mateo de Mayo, Daniel Cremers
DefVINS is a visual‑inertial odometry pipeline tailored for deformable scenes, breaking the rigidity assumption of traditional VIO. It decomposes the odometry state into a rigid, IMU‑anchored component and a non‑rigid scene warp using an embedded deformation graph. The authors also introduce VIMandala, the first real‑world benchmark with ground‑truth camera poses for deformable VIO, and extend the synthetic Drunkard’s benchmark with inertial data, demonstrating that DefVINS outperforms both rigid and non‑rigid baselines.
By Samuel Cerezo, Javier Civera
arXiv:2607. 20785v1 Announce Type: cross Abstract: Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently.
By Arjun Majumdar, Avinash Sooriyarachchi, Benjamin Tibi, Chris Bamford, Elliot Chane-Sane, Guillaume Lample, Khyathi Raghavi Chandu, Ludovic Ho Fuh, Mathieu Poiree, Olivier Duchenne, Rosalie Millner, Srijan Mishra, Theo Cachet, Thomas Chabal
arXiv:2601. 03040v2 Announce Type: replace-cross Abstract: A fundamental requirement for full autonomy is the ability to sustain accurate navigation in the absence of external data, such as GNSS signals or visual information.
By Arup Kumar Sahoo, Itzik Klein