Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching

• URL: http://arxiv.org/abs/2201.05977v1
• Date: Sun, 16 Jan 2022 05:47:07 GMT
• Title: Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching
• Authors: Fan Wang, Chaofan Zhang, Fulin Tang, Hongkui Jiang, Yihong Wu, and Yong Liu
• Abstract summary: We present a novel lightweight object-level mapping and localization method with high accuracy and robustness. We use object-level features with both semantic and geometric information to model landmarks in the environment. Based on the proposed map, the robust localization is achieved by constructing a novel local semantic scene graph descriptor.
• Score: 19.706907816202946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mapping and localization are two essential tasks for mobile robots in real-world applications. However, largescale and dynamic scenes challenge the accuracy and robustness of most current mature solutions. This situation becomes even worse when computational resources are limited. In this paper, we present a novel lightweight object-level mapping and localization method with high accuracy and robustness. Different from previous methods, our method does not need a prior constructed precise geometric map, which greatly releases the storage burden, especially for large-scale navigation. We use object-level features with both semantic and geometric information to model landmarks in the environment. Particularly, a learning topological primitive is first proposed to efficiently obtain and organize the object-level landmarks. On the basis of this, we use a robot-centric mapping framework to represent the environment as a semantic topology graph and relax the burden of maintaining global consistency at the same time. Besides, a hierarchical memory management mechanism is introduced to improve the efficiency of online mapping with limited computational resources. Based on the proposed map, the robust localization is achieved by constructing a novel local semantic scene graph descriptor, and performing multi-constraint graph matching to compare scene similarity. Finally, we test our method on a low-cost embedded platform to demonstrate its advantages. Experimental results on a large scale and multi-session real-world environment show that the proposed method outperforms the state of arts in terms of lightweight and robustness.

Related papers

• Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information [68.10033984296247]
  This paper explores the domain of active localization, emphasizing the importance of viewpoint selection to enhance localization accuracy. Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications.
  arXiv  Detail & Related papers  (2024-07-22T12:32:09Z)
• PRISM-TopoMap: Online Topological Mapping with Place Recognition and Scan Matching [42.74395278382559]
  This paper introduces PRISM-TopoMap -- a topological mapping method that maintains a graph of locally aligned locations. The proposed method involves learnable multimodal place recognition paired with the scan matching pipeline for localization and loop closure. We conduct a broad experimental evaluation of the suggested approach in a range of photo-realistic environments and on a real robot.
  arXiv  Detail & Related papers  (2024-04-02T06:25:16Z)
• Mapping High-level Semantic Regions in Indoor Environments without Object Recognition [50.624970503498226]
  The present work proposes a method for semantic region mapping via embodied navigation in indoor environments. To enable region identification, the method uses a vision-to-language model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location.
  arXiv  Detail & Related papers  (2024-03-11T18:09:50Z)
• GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in Cluttered Scenes [0.5755004576310334]
  GraNet is a graph-based grasp pose generation framework that translates a point cloud scene into multi-level graphs. Our pipeline can thus characterize the spatial distribution of grasps in cluttered scenes, leading to a higher rate of effective grasping. Our method achieves state-of-the-art performance on the large-scale GraspNet-1Billion benchmark, especially in grasping unseen objects.
  arXiv  Detail & Related papers  (2023-12-06T08:36:29Z)
• Volumetric Semantically Consistent 3D Panoptic Mapping [77.13446499924977]
  We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating semantic 3D maps suitable for autonomous agents in unstructured environments. It introduces novel ways of integrating semantic prediction confidence during mapping, producing semantic and instance-consistent 3D regions. The proposed method achieves accuracy superior to the state of the art on public large-scale datasets, improving on a number of widely used metrics.
  arXiv  Detail & Related papers  (2023-09-26T08:03:10Z)
• Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition [54.23513799338309]
  We present an Adaptive Local-Component-aware Graph Convolutional Network for skeleton-based action recognition. Our method provides a stronger representation than the global embedding and helps our model reach state-of-the-art.
  arXiv  Detail & Related papers  (2022-09-21T02:33:07Z)
• Unsupervised Metric Relocalization Using Transform Consistency Loss [66.19479868638925]
  Training networks to perform metric relocalization traditionally requires accurate image correspondences. We propose a self-supervised solution, which exploits a key insight: localizing a query image within a map should yield the same absolute pose, regardless of the reference image used for registration. We evaluate our framework on synthetic and real-world data, showing our approach outperforms other supervised methods when a limited amount of ground-truth information is available.
  arXiv  Detail & Related papers  (2020-11-01T19:24:27Z)
• DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization [27.294822556484345]
  Long-term visual localization under changing environments is a challenging problem in autonomous driving and mobile robotics. We propose a novel multi-task architecture to fuse the geometric and semantic information into the multi-scale latent embedding representation for visual place recognition.
  arXiv  Detail & Related papers  (2020-10-01T17:44:25Z)
• Locally induced Gaussian processes for large-scale simulation experiments [0.0]
  We show how placement of inducing points and their multitude can be thwarted by pathologies. Our proposed methodology hybridizes global inducing point and data subset-based local GP approximation. We show that local inducing points extend their global and data-subset component parts on the accuracy--computational efficiency frontier.
  arXiv  Detail & Related papers  (2020-08-28T21:37:46Z)
• Spatial Pyramid Based Graph Reasoning for Semantic Segmentation [67.47159595239798]
  We apply graph convolution into the semantic segmentation task and propose an improved Laplacian. The graph reasoning is directly performed in the original feature space organized as a spatial pyramid. We achieve comparable performance with advantages in computational and memory overhead.
  arXiv  Detail & Related papers  (2020-03-23T12:28:07Z)

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.