Compositional Scalable Object SLAM

• URL: http://arxiv.org/abs/2011.02658v1
• Date: Thu, 5 Nov 2020 04:46:25 GMT
• Title: Compositional Scalable Object SLAM
• Authors: Akash Sharma, Wei Dong, and Michael Kaess
• Abstract summary: We present a fast, scalable, and accurate Simultaneous Localization and Mapping (SLAM) system that represents indoor scenes as a graph of objects. We show that a compositional scalable object mapping formulation is amenable to a robust SLAM solution for drift-free large scale indoor reconstruction.
• Score: 29.349829139625403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a fast, scalable, and accurate Simultaneous Localization and Mapping (SLAM) system that represents indoor scenes as a graph of objects. Leveraging the observation that artificial environments are structured and occupied by recognizable objects, we show that a compositional scalable object mapping formulation is amenable to a robust SLAM solution for drift-free large scale indoor reconstruction. To achieve this, we propose a novel semantically assisted data association strategy that obtains unambiguous persistent object landmarks, and a 2.5D compositional rendering method that enables reliable frame-to-model RGB-D tracking. Consequently, we deliver an optimized online implementation that can run at near frame rate with a single graphics card, and provide a comprehensive evaluation against state of the art baselines. An open source implementation will be provided at https://placeholder.

Related papers

• Go-SLAM: Grounded Object Segmentation and Localization with Gaussian Splatting SLAM [12.934788858420752]
  Go-SLAM is a novel framework that utilizes 3D Gaussian Splatting SLAM to reconstruct dynamic environments. Our system facilitates open-vocabulary querying, allowing users to locate objects using natural language descriptions.
  arXiv  Detail & Related papers  (2024-09-25T13:56:08Z)
• FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects [55.77542145604758]
  FoundationPose is a unified foundation model for 6D object pose estimation and tracking. Our approach can be instantly applied at test-time to a novel object without fine-tuning.
  arXiv  Detail & Related papers  (2023-12-13T18:28:09Z)
• DNS SLAM: Dense Neural Semantic-Informed SLAM [92.39687553022605]
  DNS SLAM is a novel neural RGB-D semantic SLAM approach featuring a hybrid representation. Our method integrates multi-view geometry constraints with image-based feature extraction to improve appearance details. Our experimental results achieve state-of-the-art performance on both synthetic data and real-world data tracking.
  arXiv  Detail & Related papers  (2023-11-30T21:34:44Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• An Online Semantic Mapping System for Extending and Enhancing Visual SLAM [2.538209532048867]
  We present a real-time semantic mapping approach for mobile vision systems with a 2D to 3D object detection pipeline and rapid data association for generated landmarks. Our system reaches real-time capabilities with an average iteration duration of 65ms and is able to improve the pose estimation of a state-of-the-art SLAM by up to 68% on a public dataset.
  arXiv  Detail & Related papers  (2022-03-08T09:14:37Z)
• Combining Local and Global Pose Estimation for Precise Tracking of Similar Objects [2.861848675707602]
  We present a multi-object 6D detection and tracking pipeline for potentially similar and non-textured objects. A new network architecture, trained solely with synthetic images, allows simultaneous pose estimation of multiple objects. We show how the system can be used in a real AR assistance application within the field of construction.
  arXiv  Detail & Related papers  (2022-01-31T14:36:57Z)
• DSP-SLAM: Object Oriented SLAM with Deep Shape Priors [16.867669408751507]
  We propose an object-oriented SLAM system that builds a rich and accurate joint map of dense 3D models for foreground objects. DSP-SLAM takes as input the 3D point cloud reconstructed by a feature-based SLAM system. Our evaluation shows improvements in object pose and shape reconstruction with respect to recent deep prior-based reconstruction methods.
  arXiv  Detail & Related papers  (2021-08-21T10:00:12Z)
• 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)
• SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion [86.77318031029404]
  We propose a framework that performs scene reconstruction and semantic scene completion jointly in an incremental and real-time manner. Our framework relies on a novel neural architecture designed to process occupancy maps and leverages voxel states to accurately and efficiently fuse semantic completion with the 3D global model.
  arXiv  Detail & Related papers  (2020-10-26T15:31:52Z)
• Category Level Object Pose Estimation via Neural Analysis-by-Synthesis [64.14028598360741]
  In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module. The image synthesis network is designed to efficiently span the pose configuration space. We experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone.
  arXiv  Detail & Related papers  (2020-08-18T20:30:47Z)
• Object-based Illumination Estimation with Rendering-aware Neural Networks [56.01734918693844]
  We present a scheme for fast environment light estimation from the RGBD appearance of individual objects and their local image areas. With the estimated lighting, virtual objects can be rendered in AR scenarios with shading that is consistent to the real scene.
  arXiv  Detail & Related papers  (2020-08-06T08:23:19Z)

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.