TANGO-VIO: Triangulation-Aware Navigation with Guaranteed Feature-Observability for Visual-Inertial Odometry
- URL: http://arxiv.org/abs/2608.02079v1
- Date: Mon, 03 Aug 2026 11:32:30 GMT
- Title: TANGO-VIO: Triangulation-Aware Navigation with Guaranteed Feature-Observability for Visual-Inertial Odometry
- Abstract summary: This study presents TANGO-VIO, a triangulation-aware navigation framework that embeds a log-determinant metric of the feature-wise stacked-bearing matrix into a control barrier function.<n>The proposed architecture is evaluated through software-inthe- loop simulations and real flight experiments.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In vision-aided navigation and visual-inertial odometry, the quality of triangulated three-dimensional feature positions is a fundamental prerequisite for state estimation accuracy. Triangulation becomes ill-conditioned or even impossible when a camera undergoes pure rotation without translation, or when the observed bearing vectors provide insufficient parallax. Even though visual-inertial odometry has been extensively studied, the active maintenance of feature-observability during navigation has not been sufficiently addressed in the literature. To address this gap, this study presents TANGO-VIO, a triangulation-aware navigation framework that embeds a log-determinant metric of the feature-wise stacked-bearing matrix into a control barrier function. In this proposed method, the observability guarantee is established in the feature-geometric sense by enforcing a lower bound on the aggregate triangulation-information metric through a nominal-direction-weighted minimum-deviation velocity correction. The proposed architecture is evaluated through software-inthe- loop simulations and real flight experiments. The results show improved triangulation conditioning under low-parallax motion, while the flight response closely reproduces the corresponding simulation behavior and confirms the practical realizability of the proposed safety filter. Supplementary materials are available on the project webpage.
Related papers
- A Sliding Window Filter on the Galilean Group for Consistent Aided Inertial Navigation with Unknown Measurement Delays [9.474491558495359]
We study aided navigation when the aiding sensor measurements are subject to an unknown constant delay.<n>The goal is to estimate the delay and navigation state jointly so that delayed measurements correct the trajectory at the appropriate times.
arXiv Detail & Related papers (2026-08-30T02:34:28Z) - Does Robust VIO Need More Learning? Geometry-Verified Visual Measurements under Distribution Shift [18.007082445726756]
We propose a minimal-learning stereo VIO framework in which SEA-RAFT is used only to propose dense stereo correspondences and predict their uncertainty.<n>Dense flow is sampled at sparse feature locations, filtered using predicted uncertainty and stereo epipolar consistency, and incorporated into a sliding-window stereo-inertial estimator.<n>Experiments on EuRoC, VIODE, and 4Seasons demonstrate accurate and stable estimation under motion blur, dynamic scenes, illumination changes, and large indoor-to-outdoor distribution shifts.
arXiv Detail & Related papers (2026-07-20T13:57:44Z) - Attitude Estimation Using Inertial and Barometric Measurements [0.0]
This paper introduces a barometer-aided attitude estimation architecture that exploits barometric altitude measurements to provide complementary information on the vehicle's vertical motion.<n>Results demonstrate that barometer-aided estimation provides a lightweight, reliable, and effective complementary sensing modality for attitude estimation in minimal-sensing configurations.
arXiv Detail & Related papers (2026-07-14T20:36:13Z) - IoUCert: Robustness Verification for Anchor-based Object Detectors [58.35703549470485]
We introduce IoUCert, a novel formal verification framework designed specifically to overcome these bottlenecks in anchor-based object detection architectures.<n>We show that our method enables the robustness verification of realistic, anchor-based models including SSD, YOLOv2, and YOLOv3 variants against various input perturbations.
arXiv Detail & Related papers (2026-03-03T14:36:46Z) - Vision-Aided Relative State Estimation for Approach and Landing on a Moving Platform with Inertial Measurements [36.94429692322632]
This paper tackles the problem of estimating the relative position, orientation, and velocity between a UAV and a planar platform undergoing arbitrary 3D motion during approach and landing.<n>We propose a cascade observer with a complementary filter on SO(3) to reconstruct the relative attitude, followed by a linear Riccati observer for relative position and velocity estimation.<n>The performance of the proposed observers is validated through extensive simulations.
arXiv Detail & Related papers (2025-12-22T10:28:20Z) - Curriculum-Based Reinforcement Learning for Autonomous UAV Navigation in Unknown Curved Tubular Conduit [0.0]
We propose a reinforcement learning approach enabling a drone to navigate unknown three-dimensional tubes without any prior knowledge of their geometry.<n>The Pure Pursuit algorithm, used as a deterministic baseline, benefits from explicit access to the centerline.<n>A turning-negotiation mechanism, based on the combination of direct visibility, directional memory, and LiDAR symmetry cues, proves essential for ensuring stable navigation.
arXiv Detail & Related papers (2025-12-11T18:57:29Z) - Cascaded Tightly-Coupled Observer Design for Single-Range-Aided Inertial Navigation [0.0]
This work introduces a single-range-aided navigation observer that reconstructs the full state of a rigid body using only an Inertial Measurement Unit (IMU)<n>The design first formulates an extended linear time-varying (LTV) system to estimate body-frame position, body-frame velocity, and the gravity direction.<n>Almost Global Asymptotic Stability (AGAS) of the cascaded design is established under a uniform observability condition, ensuring robustness to sensor noise and trajectory variations.
arXiv Detail & Related papers (2025-12-05T22:44:22Z) - SPAN: Spatial-Projection Alignment for Monocular 3D Object Detection [49.12928389918159]
Existing monocular 3D detectors typically tame the pronounced nonlinear regression of 3D bounding box through decoupled prediction paradigm.<n>We propose novel Spatial-Projection Alignment (SPAN) with two pivotal components.<n>SPAN enforces an explicit global spatial constraint between the predicted and ground-truth 3D bounding boxes, thereby rectifying spatial drift caused by decoupled attribute regression.<n>3D-2D Projection Alignment ensures that the projected 3D box is aligned tightly within its corresponding 2D detection bounding box on the image plane, mitigating projection misalignment overlooked in previous works.
arXiv Detail & Related papers (2025-11-10T04:48:48Z) - Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detection [64.0168648353038]
3D anomaly detection in point-cloud data is critical for industrial quality control, aiming to identify structural defects with high reliability.<n>Current memory bank-based methods often suffer from inconsistent feature transformations and limited discriminative capacity.<n>We propose a registration-induced, rotation-invariant feature extraction framework that integrates the objectives of point-cloud registration and memory-based anomaly detection.
arXiv Detail & Related papers (2025-10-19T14:56:38Z) - EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation [59.33052312107478]
Event cameras offer possibilities for 3D motion estimation through continuous adaptive pixel-level responses to scene changes.<n>This paper presents EMove, a novel event-based framework that models-uniform trajectories via event-guided parametric curves.<n>For motion representation, we introduce a density-aware adaptation mechanism to fuse spatial and temporal features under event guidance.<n>The final 3D motion estimation is achieved through multi-temporal sampling of parametric trajectories, flows and depth motion fields.
arXiv Detail & Related papers (2025-03-14T13:15:54Z) - GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection [92.41859045360532]
We propose a novel Geometry Uncertainty Propagation Network (GUPNet++)<n>It models the uncertainty propagation relationship of the geometry projection during training, improving the stability and efficiency of the end-to-end model learning.<n> Experiments show that the proposed approach not only obtains (state-of-the-art) SOTA performance in image-based monocular 3D detection but also demonstrates superiority in efficacy with a simplified framework.
arXiv Detail & Related papers (2023-10-24T08:45:15Z) - OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object
Detection [51.153003057515754]
OPA-3D is a single-stage, end-to-end, Occlusion-Aware Pixel-Wise Aggregation network.
It jointly estimates dense scene depth with depth-bounding box residuals and object bounding boxes.
It outperforms state-of-the-art methods on the main Car category.
arXiv Detail & Related papers (2022-11-02T14:19:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.