Abstract
We present AnchorFlow-VIO, an open-source Basalt-derived monocular visual-inertial odometry system, and evaluate a uniform configuration on LaMAria. The system uses one grayscale camera and one IMU, combining factory IMU correction, a fixed timing adjustment, windowed initialization and fixed-template patch tracking. Server evaluation of all 63 test recordings yields control-point scores of 47.2, 37.1, 24.8, 35.1 and 15.6 for Short, Medium, Long, Low light and Moving platform. Test pose recalls at 5 m are 84.6%, 69.1%, 53.9% and 62.9% for the four categories reporting dense recall. On ten training recordings, mean control-point score is 41.38 and all 265,694 image timestamps receive poses, including 204 filled poses. One training platform recording diverges and scores zero. A paired four-record comparison favors fixed over rolling templates on three recordings, while reversing that ranking on the fourth. Increasing feature density also yields mixed results. Ten fresh runs at prospectively fixed 30 s and 120 s offsets expose substantial start sensitivity: a low-light recording scores 6.14 at the earlier start and 42.00 at the later start. These results characterize the limits of the frozen system and show why complete output, feature counts and selected successful runs are insufficient evidence of robust localization. Source code, frozen configuration and reproducible submission tools are publicly available; training ablations are reported separately from the submitted test evaluation.
More in this topicVisual-inertial odometry estimates camera motion by combining image geometry with inertial measurements for metric navigation.
See all 57 papers → Oct 10, 2026
Feed-forward geometric foundation models (GFMs) directly predict dense geometry and camera poses from batches of images. Emerging visual SLAM methods chain together consecutive GFM batches to form dense point cloud maps. A core challenge is how to address the ambiguous geometric scale of GFM predictions: independent predictions are locally consistent up-to-scale but not constrained by any direct physical measurements. Models trained on data with known scale improve the consistency of GFM reconstructions but again do not directly measure it. Consequently, trajectories and 3D reconstructions from GFM-enabled SLAM methods inherit this issue. Motivated by parallels with classical visual-inertial odometry, we introduce FoundationVIO which integrates inertial sensing with GFM predictions to recover metric scale reconstruction using a factor-graph based approach. We demonstrate odometry estimates competitive with classical state-of-the-art VIO methods and accurate dense reconstructions that outperform GFM-enabled SLAM methods. We also add a minimal GFM-based loop-closure mechanism to create a full Visual-Inertial SLAM system capable of creating globally accurate reconstructions and pose estimates. We evaluate our method on three datasets with well known sensor systems and demonstrate its compatibility on ubiquitous camera and IMU sensors. The method runs at approximately real-time on a consumer-grade laptop GPU. Code: https://github.com/ori-drs/FoundationVIO.
Jin Rhee, Yuhao Zhang, Frank Dellaert +1
Oxford Robotics Institute, University of Oxford, U.K. · School of Interactive Computing, Georgia Institute of Technology, USA
Jan 2, 2026
Deformable scenes violate the rigidity assumptions underpinning classical visual--inertial odometry (VIO), often leading to over-fitting to local non-rigid motion or to severe camera pose drift when deformation dominates visual parallax. In this paper, we introduce DefVINS, the first visual-inertial odometry pipeline designed to operate in deformable environments. Our approach models the odometry state by decomposing it into a rigid, IMU-anchored component and a non-rigid scene warp represented by an embedded deformation graph. As a second contribution, we present VIMandala, the first benchmark containing real images and ground-truth camera poses for visual-inertial odometry in deformable scenes. In addition, we augment the synthetic Drunkard's benchmark with simulated inertial measurements to further evaluate our pipeline under controlled conditions. We also provide an observability analysis of the visual-inertial deformable odometry problem, characterizing how inertial measurements constrain camera motion and render otherwise unobservable modes identifiable in the presence of deformation. This analysis motivates the use of IMU anchoring and leads to a conditioning-based activation strategy that avoids ill-posed updates under poor excitation. Experimental results on both the synthetic Drunkard's and our real VIMandala benchmarks show that DefVINS outperforms rigid visual--inertial and non-rigid visual odometry baselines. Our source code and data will be released upon acceptance.
Samuel Cerezo, Javier Civera
I3A, Universidad de Zaragoza, Spain
Jun 17, 2026
This paper presents an analysis of the observability and consistency properties of filtering-based visual-inertial navigation systems (VINS) that utilize anchored feature representations. The unobservable subspace of VINS with anchored landmark parameterizations is shown to be independent of the estimated landmark state, which leads to improved estimator consistency properties without any additional modifications. However, the unobservable subspace is still found to depend on the estimated navigation state, necessitating additional consistency-enforcing techniques. Two methods to improve the consistency of VINS with anchored feature representations are presented. Simulation results showcase that all estimators employing anchored feature paramterizations exhibit improved consistency properties compared to algorithms that estimate features resolved in a global reference frame, especially in scenarios where feature initialization may be poor. Real-world experiments on the TUM-VI dataset showcase that the use of anchored feature representations alone can yield comparable performance to consistency-improved estimators employing a global feature representation, demonstrating the benefit of using anchored feature parameterizations for VINS.
Mitchell Cohen, Vassili Korotkine, James Richard Forbes
Department of Mechanical Engineering, McGill University, Montreal, QC H3A 0C3, Canada