GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization

• URL: http://arxiv.org/abs/2004.12051v2
• Date: Sun, 24 May 2020 01:59:57 GMT
• Title: GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization
• Authors: Sicong Du, Hengkai Guo, Yao Chen, Yilun Lin, Xiangbing Meng, Linfu Wen, Fei-Yue Wang
• Abstract summary: The algorithm starts by homography estimation in a sliding window. The proposed method fully exploits the plane information from multiple frames and avoids the ambiguities in homography decomposition. Experimental results show that our method outperforms the fine-tuned baselines in both accuracy and real-time.
• Score: 22.847353792031488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Initialization is essential to monocular Simultaneous Localization and Mapping (SLAM) problems. This paper focuses on a novel initialization method for monocular SLAM based on planar features. The algorithm starts by homography estimation in a sliding window. It then proceeds to a global plane optimization (GPO) to obtain camera poses and the plane normal. 3D points can be recovered using planar constraints without triangulation. The proposed method fully exploits the plane information from multiple frames and avoids the ambiguities in homography decomposition. We validate our algorithm on the collected chessboard dataset against baseline implementations and present extensive analysis. Experimental results show that our method outperforms the fine-tuned baselines in both accuracy and real-time.

Related papers

• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• Plane2Depth: Hierarchical Adaptive Plane Guidance for Monocular Depth Estimation [38.81275292687583]
  We propose Plane2Depth, which adaptively utilizes plane information to improve depth prediction within a hierarchical framework. In the proposed plane guided depth generator (PGDG), we design a set of plane queries as prototypes to softly model planes in the scene and predict per-pixel plane coefficients. In the proposed adaptive plane query aggregation (APGA) module, we introduce a novel feature interaction approach to improve the aggregation of multi-scale plane features.
  arXiv  Detail & Related papers  (2024-09-04T07:45:06Z)
• AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings [26.845588648999417]
  We tackle the problem of estimating the planar surfaces in a 3D scene from posed images. We propose a method that predicts multi-view consistent plane embeddings that complement geometry when clustering points into planes. We show through extensive evaluation on the ScanNetV2 dataset that our new method outperforms existing approaches.
  arXiv  Detail & Related papers  (2024-06-13T09:49:31Z)
• Loopy-SLAM: Dense Neural SLAM with Loop Closures [53.11936461015725]
  We introduce Loopy-SLAM that globally optimize poses and the dense 3D model. We use frame-to-model tracking using a data-driven point-based submap generation method and trigger loop closures online by performing global place recognition. Evaluation on the synthetic Replica and real-world TUM-RGBD and ScanNet datasets demonstrate competitive or superior performance in tracking, mapping, and rendering accuracy when compared to existing dense neural RGBD SLAM methods.
  arXiv  Detail & Related papers  (2024-02-14T18:18:32Z)
• 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)
• Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection [92.75961303269548]
  The ground plane prior is a very informative geometry clue in monocular 3D object detection (M3OD) We propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go. Our GPENet can outperform other methods and achieve state-of-the-art performance, well demonstrating the effectiveness and the superiority of the proposed approach.
  arXiv  Detail & Related papers  (2022-11-03T02:21:35Z)
• Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry [7.169216737580712]
  We develop a lightweight scale recovery framework leveraging an accurate and robust estimation of the ground plane. Experiments on the KITTI dataset show that the proposed framework can achieve state-of-theart accuracy in terms of translation errors. Due to the light-weight design, our framework also demonstrates a high frequency of 20Hz on the dataset.
  arXiv  Detail & Related papers  (2021-01-15T07:21:24Z)
• Efficient Initial Pose-graph Generation for Global SfM [56.38930515826556]
  We propose ways to speed up the initial pose-graph generation for global Structure-from-Motion algorithms. The algorithms are tested on 402130 image pairs from the 1DSfM dataset.
  arXiv  Detail & Related papers  (2020-11-24T09:32:03Z)
• Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection [63.942632088208505]
  We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image. This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects.
  arXiv  Detail & Related papers  (2020-03-28T18:51:43Z)
• Monocular Direct Sparse Localization in a Prior 3D Surfel Map [18.567015858362208]
  We introduce an approach to tracking the pose of a monocular camera in a prior surfel map. The tracked points with and without the global planar information involve both global and local constraints of frames to the system. Our approach formulates all constraints in the form of direct photometric errors within a local window of the frames.
  arXiv  Detail & Related papers  (2020-02-23T15:29:38Z)
• Plane Pair Matching for Efficient 3D View Registration [7.920114031312631]
  We present a novel method to estimate the motion matrix between overlapping pairs of 3D views in the context of indoor scenes. We use the Manhattan world assumption to introduce lightweight geometric constraints under the form of planes quadri into the problem. We validate our approach on a toy example and present quantitative experiments on a public RGB-D dataset, comparing against recent state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-20T11:15:26Z)

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.