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
arXiv:2609.30459v1 Announce Type: cross
Abstract: Perception in robotics and XR fundamentally relies on good state estimation. Visual-inertial odometry (VIO) and Simultaneous Localization and Mapping...
By Ole Hoffmann, Mateo de Mayo, Daniel Cremers
The paper introduces a minimalist visual-inertial odometry system that uses only four downward-facing photodiodes with optical Gabor masks and an IMU to estimate motion for differential-drive robots. By jointly optimizing mask parameters and a Temporal Convolutional Network in a physically-grounded simulator, the model decodes speed from the photodiode signals and combines it with IMU angular speed to produce a continuous planar trajectory. Experiments on a prototype robot across indoor and outdoor terrains show that the system closely follows reference trajectories without real-world fine-tuning.
By Francesco Pasti, Jeremy Klotz, Nicola Bellotto, Shree K. Nayar
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
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
DAVIO is a dense monocular‑inertial SLAM system that leverages a single multi‑view depth model (Depth Anything 3) for both initialization and mapping. It starts up quickly by solving a feature‑free linear system from a five‑image window and IMU pre‑integration, then uses a VIO filter whose metric poses condition the depth model during tracking. The system corrects residual scale along viewing rays, preserves metric baselines, and refines the map with a gravity‑preserving sub‑map graph, achieving earlier start‑up, lower localization error, and more accurate dense maps than state‑of‑the‑art feed‑forward mappers on both EuRoC and building‑scale ORI datasets.
By Jaafar Mahmoud, Arthur Movsesyan, Mikhail Iumanov, Sergey Kolyubin
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
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:2607. 11686v1 Announce Type: cross Abstract: Low-cost unmanned ground vehicles are often used in indoor places like warehouses, inspection corridors, and farm rows, where painted floor lines guide the robot.
By Jakob Solberg Berntzen, Safia Fatima, Leon Moonen
arXiv:2610.07217v1 Announce Type: cross
Abstract: Despite its promise for scaling robot learning, egocentric manipulation data is still scarce today. Collection at scale requires vertically integrati...
By Grounded Superintelligence, BitRobot
The paper introduces SLIM-init, a monocular visual‑inertial initialization method that avoids explicit 3D reconstruction by using 2D line features and vanishing points. It provides robust rotation constraints through line‑derived vanishing points and improves translation estimation with a line epipolar residual and a line‑normal projection residual. Experiments on public benchmarks and custom degenerate‑motion sequences show that SLIM-init achieves higher accuracy and robustness than existing initialization techniques.
By Junwan Choi, Woongrae Jo, Dong-Uk Seo, Jinwoo Jeon, Hyun Myung
arXiv:2611.07463v1 Announce Type: cross
Abstract: LiDAR-Inertial-Visual Odometry (LIVO) integrates complementary LiDAR, camera, and IMU measurements to achieve robust state estimation. However, most...
By Reon Tabata, Kenji Koide, Shuji Oishi, Taku Okawara, Mizuho Aoki, Aoki Takanose, Masashi Yokozuka, Jun Miura