GOReloc: Graph-based Object-Level Relocalization for Visual SLAM

• URL: http://arxiv.org/abs/2408.07917v1
• Date: Thu, 15 Aug 2024 03:54:33 GMT
• Title: GOReloc: Graph-based Object-Level Relocalization for Visual SLAM
• Authors: Yutong Wang, Chaoyang Jiang, Xieyuanli Chen,
• Abstract summary: This article introduces a novel method for object-level relocalization of robotic systems. It determines the pose of a camera sensor by robustly associating the object detections in the current frame with 3D objects in a lightweight object-level map.
• Score: 17.608119427712236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article introduces a novel method for object-level relocalization of robotic systems. It determines the pose of a camera sensor by robustly associating the object detections in the current frame with 3D objects in a lightweight object-level map. Object graphs, considering semantic uncertainties, are constructed for both the incoming camera frame and the pre-built map. Objects are represented as graph nodes, and each node employs unique semantic descriptors based on our devised graph kernels. We extract a subgraph from the target map graph by identifying potential object associations for each object detection, then refine these associations and pose estimations using a RANSAC-inspired strategy. Experiments on various datasets demonstrate that our method achieves more accurate data association and significantly increases relocalization success rates compared to baseline methods. The implementation of our method is released at \url{https://github.com/yutongwangBIT/GOReloc}.

Related papers

• Open-Vocabulary Octree-Graph for 3D Scene Understanding [54.11828083068082]
  Octree-Graph is a novel scene representation for open-vocabulary 3D scene understanding. An adaptive-octree structure is developed that stores semantics and depicts the occupancy of an object adjustably according to its shape.
  arXiv  Detail & Related papers  (2024-11-25T10:14:10Z)
• Multiview Scene Graph [7.460438046915524]
  A proper scene representation is central to the pursuit of spatial intelligence. We propose to build Multiview Scene Graphs (MSG) from unposed images. MSG represents a scene topologically with interconnected place and object nodes.
  arXiv  Detail & Related papers  (2024-10-15T02:04:05Z)
• Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection [57.883265488038134]
  We propose a hierarchical graph interaction network termed HGINet for camouflaged object detection. The network is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Our experiments demonstrate the superior performance of HGINet compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-27T12:53:25Z)
• VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks [19.789761641342043]
  We propose a Visual Object Odometry and Mapping framework VOOM. We use high-level objects and low-level points as the hierarchical landmarks in a coarse-to-fine manner. VOOM outperforms both object-oriented SLAM and feature points SLAM systems in terms of localization.
  arXiv  Detail & Related papers  (2024-02-21T08:22:46Z)
• Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency [14.694754836704819]
  We present an object-based loop closure detection method based on the spatial layout and semanic consistency of the 3D scene graph. Experimental results demonstrate that our proposed data association approach can construct more accurate 3D semantic maps.
  arXiv  Detail & Related papers  (2023-04-11T11:20:51Z)
• BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects [89.2314092102403]
  We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence. Our method works for arbitrary rigid objects, even when visual texture is largely absent.
  arXiv  Detail & Related papers  (2023-03-24T17:13:49Z)
• Learning to Generate Scene Graph from Natural Language Supervision [52.18175340725455]
  We propose one of the first methods that learn from image-sentence pairs to extract a graphical representation of localized objects and their relationships within an image, known as scene graph. We leverage an off-the-shelf object detector to identify and localize object instances, match labels of detected regions to concepts parsed from captions, and thus create "pseudo" labels for learning scene graph.
  arXiv  Detail & Related papers  (2021-09-06T03:38:52Z)
• Object-Augmented RGB-D SLAM for Wide-Disparity Relocalisation [3.888848425698769]
  We propose a novel object-augmented RGB-D SLAM system that is capable of constructing a consistent object map and performing relocalisation based on centroids of objects in the map.
  arXiv  Detail & Related papers  (2021-08-05T11:02:25Z)
• A Graph-based Interactive Reasoning for Human-Object Interaction Detection [71.50535113279551]
  We present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs. We construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet. Our framework is end-to-end trainable and free from costly annotations like human pose.
  arXiv  Detail & Related papers  (2020-07-14T09:29:03Z)
• Weakly-Supervised Salient Object Detection via Scribble Annotations [54.40518383782725]
  We propose a weakly-supervised salient object detection model to learn saliency from scribble labels. We present a new metric, termed saliency structure measure, to measure the structure alignment of the predicted saliency maps. Our method not only outperforms existing weakly-supervised/unsupervised methods, but also is on par with several fully-supervised state-of-the-art models.
  arXiv  Detail & Related papers  (2020-03-17T12:59:50Z)
• Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots [37.16690737208046]
  We argue for an approach opposite to existing methods using object-level anchors. Inspired by compositional models, we propose an object as composition of its interior non-empty voxels, termed hotspots. Based on OHS, we propose an anchor-free detection head with a novel ground truth assignment strategy.
  arXiv  Detail & Related papers  (2019-12-30T03:02:22Z)

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.