LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation

• URL: http://arxiv.org/abs/2410.04419v2
• Date: Mon, 21 Oct 2024 06:35:08 GMT
• Title: LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation
• Authors: Jianhao Jiao, Jinhao He, Changkun Liu, Sebastian Aegidius, Xiangcheng Hu, Tristan Braud, Dimitrios Kanoulas,
• Abstract summary: LiteVLoc is a visual localization framework that uses a lightweight topo-metric map to represent the environment. It reduces storage overhead by leveraging learning-based feature matching and geometric solvers for metric pose estimation.
• Score: 5.739362282280063
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents LiteVLoc, a hierarchical visual localization framework that uses a lightweight topo-metric map to represent the environment. The method consists of three sequential modules that estimate camera poses in a coarse-to-fine manner. Unlike mainstream approaches relying on detailed 3D representations, LiteVLoc reduces storage overhead by leveraging learning-based feature matching and geometric solvers for metric pose estimation. A novel dataset for the map-free relocalization task is also introduced. Extensive experiments including localization and navigation in both simulated and real-world scenarios have validate the system's performance and demonstrated its precision and efficiency for large-scale deployment. Code and data will be made publicly available.

Related papers

• Monocular Visual Place Recognition in LiDAR Maps via Cross-Modal State Space Model and Multi-View Matching [2.400446821380503]
  We introduce an efficient framework to learn descriptors for both RGB images and point clouds. It takes visual state space model (VMamba) as the backbone and employs a pixel-view-scene joint training strategy. A visible 3D points overlap strategy is then designed to quantify the similarity between point cloud views and RGB images for multi-view supervision.
  arXiv  Detail & Related papers  (2024-10-08T18:31:41Z)
• SplatLoc: 3D Gaussian Splatting-based Visual Localization for Augmented Reality [50.179377002092416]
  We propose an efficient visual localization method capable of high-quality rendering with fewer parameters. Our method achieves superior or comparable rendering and localization performance to state-of-the-art implicit-based visual localization approaches.
  arXiv  Detail & Related papers  (2024-09-21T08:46:16Z)
• Visual Localization in 3D Maps: Comparing Point Cloud, Mesh, and NeRF Representations [8.522160106746478]
  We present a global visual localization system capable of localizing a single camera image across various 3D map representations. Our system generates a database by synthesizing novel views of the scene, creating RGB and depth image pairs. NeRF synthesized images show superior performance, localizing query images at an average success rate of 72%.
  arXiv  Detail & Related papers  (2024-08-21T19:37:17Z)
• MeshVPR: Citywide Visual Place Recognition Using 3D Meshes [18.168206222895282]
  Mesh-based scene representation offers a promising direction for simplifying large-scale hierarchical visual localization pipelines. While existing work demonstrates the viability of meshes for visual localization, the impact of using synthetic databases rendered from them in visual place recognition remains largely unexplored. We propose MeshVPR, a novel VPR pipeline that utilizes a lightweight features alignment framework to bridge the gap between real-world and synthetic domains.
  arXiv  Detail & Related papers  (2024-06-04T20:45:53Z)
• Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians [87.48403838439391]
  3D Splatting has emerged as a powerful representation of geometry and appearance for RGB-only dense Simultaneous SLAM. We propose the first RGB-only SLAM system with a dense 3D Gaussian map representation. Our experiments on the Replica, TUM-RGBD, and ScanNet datasets indicate the effectiveness of globally optimized 3D Gaussians.
  arXiv  Detail & Related papers  (2024-05-26T12:26:54Z)
• 3DGS-ReLoc: 3D Gaussian Splatting for Map Representation and Visual ReLocalization [13.868258945395326]
  This paper presents a novel system designed for 3D mapping and visual relocalization using 3D Gaussian Splatting. Our proposed method uses LiDAR and camera data to create accurate and visually plausible representations of the environment.
  arXiv  Detail & Related papers  (2024-03-17T23:06:12Z)
• LISNeRF Mapping: LiDAR-based Implicit Mapping via Semantic Neural Fields for Large-Scale 3D Scenes [2.822816116516042]
  Large-scale semantic mapping is crucial for outdoor autonomous agents to fulfill high-level tasks such as planning and navigation. This paper proposes a novel method for large-scale 3D semantic reconstruction through implicit representations from posed LiDAR measurements alone.
  arXiv  Detail & Related papers  (2023-11-04T03:55:38Z)
• 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)
• MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training [58.07391711548269]
  Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training. Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training.
  arXiv  Detail & Related papers  (2023-03-23T17:59:02Z)
• TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo [55.30992853477754]
  We present TANDEM, a real-time monocular tracking and dense framework. For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding window of alignments. TANDEM shows state-of-the-art real-time 3D reconstruction performance.
  arXiv  Detail & Related papers  (2021-11-14T19:01:02Z)
• Progressive Coordinate Transforms for Monocular 3D Object Detection [52.00071336733109]
  We propose a novel and lightweight approach, dubbed em Progressive Coordinate Transforms (PCT) to facilitate learning coordinate representations. In this paper, we propose a novel and lightweight approach, dubbed em Progressive Coordinate Transforms (PCT) to facilitate learning coordinate representations.
  arXiv  Detail & Related papers  (2021-08-12T15:22:33Z)

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.