ADUGS-VINS: Generalized Visual-Inertial Odometry for Robust Navigation in Highly Dynamic and Complex Environments

• URL: http://arxiv.org/abs/2411.19289v3
• Date: Mon, 03 Mar 2025 07:18:14 GMT
• Title: ADUGS-VINS: Generalized Visual-Inertial Odometry for Robust Navigation in Highly Dynamic and Complex Environments
• Authors: Rui Zhou, Jingbin Liu, Junbin Xie, Jianyu Zhang, Yingze Hu, Jiele Zhao,
• Abstract summary: We introduce ADUGS-VINS, which integrates an enhanced SORT algorithm along with a promptable foundation model into VIO.<n>We evaluate our proposed method using multiple public datasets representing various scenes, as well as in a real-world scenario involving diverse dynamic objects.
• Score: 7.07379964916809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual-inertial odometry (VIO) is widely used in various fields, such as robots, drones, and autonomous vehicles. However, real-world scenes often feature dynamic objects, compromising the accuracy of VIO. The diversity and partial occlusion of these objects present a tough challenge for existing dynamic VIO methods. To tackle this challenge, we introduce ADUGS-VINS, which integrates an enhanced SORT algorithm along with a promptable foundation model into VIO, thereby improving pose estimation accuracy in environments with diverse dynamic objects and frequent occlusions. We evaluated our proposed method using multiple public datasets representing various scenes, as well as in a real-world scenario involving diverse dynamic objects. The experimental results demonstrate that our proposed method performs impressively in multiple scenarios, outperforming other state-of-the-art methods. This highlights its remarkable generalization and adaptability in diverse dynamic environments, showcasing its potential to handle various dynamic objects in practical applications.

Related papers

• MTGS: Multi-Traversal Gaussian Splatting [51.22657444433942]
  Multi-traversal data provides multiple viewpoints for scene reconstruction within a road block. We propose Multi-Traversal Gaussian Splatting (MTGS), a novel approach that reconstructs high-quality driving scenes from arbitrarily collected multi-traversal data. Our results demonstrate that MTGS improves LPIPS by 23.5% and geometry accuracy by 46.3% compared to single-traversal baselines.
  arXiv  Detail & Related papers  (2025-03-16T15:46:12Z)
• UrbanGS: Semantic-Guided Gaussian Splatting for Urban Scene Reconstruction [86.4386398262018]
  UrbanGS uses 2D semantic maps and an existing dynamic Gaussian approach to distinguish static objects from the scene. For potentially dynamic objects, we aggregate temporal information using learnable time embeddings. Our approach outperforms state-of-the-art methods in reconstruction quality and efficiency.
  arXiv  Detail & Related papers  (2024-12-04T16:59:49Z)
• DynaVINS++: Robust Visual-Inertial State Estimator in Dynamic Environments by Adaptive Truncated Least Squares and Stable State Recovery [11.37707868611451]
  We propose a robust VINS framework called mboxtextitDynaVINS++. Our approach shows promising performance in dynamic environments, including scenes with abruptly dynamic objects.
  arXiv  Detail & Related papers  (2024-10-20T12:13:45Z)
• OmniRe: Omni Urban Scene Reconstruction [78.99262488964423]
  We introduce OmniRe, a comprehensive system for creating high-fidelity digital twins of dynamic real-world scenes from on-device logs. Our approach builds scene graphs on 3DGS and constructs multiple Gaussian representations in canonical spaces that model various dynamic actors.
  arXiv  Detail & Related papers  (2024-08-29T17:56:33Z)
• DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments [28.23284296418962]
  Zero-Shot Object Navigation (ZSON) requires agents to autonomously locate and approach unseen objects in unfamiliar environments. Existing datasets for developing ZSON algorithms lack consideration of dynamic obstacles, object diversity, and scene texts. We propose a dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments (DOZE) DOZE comprises ten high-fidelity 3D scenes with over 18k tasks, aiming to mimic complex, dynamic real-world scenarios.
  arXiv  Detail & Related papers  (2024-02-29T10:03:57Z)
• Prompt-Driven Dynamic Object-Centric Learning for Single Domain Generalization [61.64304227831361]
  Single-domain generalization aims to learn a model from single source domain data to achieve generalized performance on other unseen target domains. We propose a dynamic object-centric perception network based on prompt learning, aiming to adapt to the variations in image complexity.
  arXiv  Detail & Related papers  (2024-02-28T16:16:51Z)
• Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes [11.56518009058007]
  Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems. meta-heuristics have shown promising effectiveness in static clustering tasks, their application for tracking optimal clustering solutions or robust clustering over time in dynamic environments remains largely underexplored. This is partly due to a lack of dynamic datasets with diverse, controllable, and realistic dynamic characteristics, hindering systematic performance evaluations of clustering algorithms in various dynamic scenarios. This deficiency leads to a gap in our understanding and capability to effectively design algorithms for clustering in dynamic environments.
  arXiv  Detail & Related papers  (2024-02-24T05:49:27Z)
• ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection [70.11264880907652]
  Recent object (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. We propose an effective unified collaborative pyramid network that mimics human behavior when observing vague images and camouflaged zooming in and out. Our framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks.
  arXiv  Detail & Related papers  (2023-10-31T06:11:23Z)
• Dynamic-Resolution Model Learning for Object Pile Manipulation [33.05246884209322]
  We investigate how to learn dynamic and adaptive representations at different levels of abstraction to achieve the optimal trade-off between efficiency and effectiveness. Specifically, we construct dynamic-resolution particle representations of the environment and learn a unified dynamics model using graph neural networks (GNNs) We show that our method achieves significantly better performance than state-of-the-art fixed-resolution baselines at the gathering, sorting, and redistribution of granular object piles.
  arXiv  Detail & Related papers  (2023-06-29T05:51:44Z)
• Vector Autoregressive Evolution for Dynamic Multi-Objective Optimisation [7.5104598146227]
  Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple objectives in varying environments. This paper proposes vector autoregressive evolution (VARE) consisting of vector autoregression ( VAR) and environment-aware hypermutation to address environmental changes in DMO.
  arXiv  Detail & Related papers  (2023-05-22T06:24:25Z)
• Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes [51.20150148066458]
  We propose a novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the generalizationally crafted masks. Experiments on real-world datasets prove the significant effectiveness and ability of the proposed method.
  arXiv  Detail & Related papers  (2023-04-18T13:55:24Z)
• Learning Robust Dynamics through Variational Sparse Gating [18.476155786474358]
  In environments with many objects, often only a small number of them are moving or interacting at the same time. In this paper, we investigate integrating this inductive bias of sparse interactions into the latent dynamics of world models trained from pixels.
  arXiv  Detail & Related papers  (2022-10-21T02:56:51Z)
• Scalable Video Object Segmentation with Identification Mechanism [125.4229430216776]
  This paper explores the challenges of achieving scalable and effective multi-object modeling for semi-supervised Video Object (VOS) We present two innovative approaches, Associating Objects with Transformers (AOT) and Associating Objects with Scalable Transformers (AOST) Our approaches surpass the state-of-the-art competitors and display exceptional efficiency and scalability consistently across all six benchmarks.
  arXiv  Detail & Related papers  (2022-03-22T03:33:27Z)
• ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation [135.10594078615952]
  We introduce ACID, an action-conditional visual dynamics model for volumetric deformable objects. A benchmark contains over 17,000 action trajectories with six types of plush toys and 78 variants. Our model achieves the best performance in geometry, correspondence, and dynamics predictions.
  arXiv  Detail & Related papers  (2022-03-14T04:56:55Z)
• TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction [57.1209039399599]
  We propose a map representation that allows maintaining a single volume for the entire scene and all the objects therein. In a multiple dynamic object tracking and reconstruction scenario, our representation allows maintaining accurate reconstruction of surfaces even while they become temporarily occluded by other objects moving in their proximity. We evaluate the proposed TSDF++ formulation on a public synthetic dataset and demonstrate its ability to preserve reconstructions of occluded surfaces when compared to the standard TSDF map representation.
  arXiv  Detail & Related papers  (2021-05-16T16:15:05Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments [14.260575326111585]
  We present RP-VIO, a state-of-the-art visual-inertial odometry system for dynamic environments. We also present a highly-dynamic, photorealistic synthetic dataset for a more effective evaluation of the capabilities of modern VINS systems.
  arXiv  Detail & Related papers  (2021-03-18T17:33:07Z)
• DOT: Dynamic Object Tracking for Visual SLAM [83.69544718120167]
  DOT combines instance segmentation and multi-view geometry to generate masks for dynamic objects. To determine which objects are actually moving, DOT segments first instances of potentially dynamic objects and then, with the estimated camera motion, tracks such objects by minimizing the photometric reprojection error. Our results show that our approach improves significantly the accuracy and robustness of ORB-SLAM 2, especially in highly dynamic scenes.
  arXiv  Detail & Related papers  (2020-09-30T18:36:28Z)
• Multi-object Monocular SLAM for Dynamic Environments [12.537311048732017]
  The term multibody, implies that we track the motion of the camera, as well as that of other dynamic participants in the scene. Existing approaches solve restricted variants of the problem, but the solutions suffer relative scale ambiguity. We propose a multi pose-graph optimization formulation, to resolve the relative and absolute scale factor ambiguities involved.
  arXiv  Detail & Related papers  (2020-02-10T03:49:16Z)

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.