Geometric Structure Aided Visual Inertial Localization
- URL: http://arxiv.org/abs/2011.04173v1
- Date: Mon, 9 Nov 2020 03:48:39 GMT
- Title: Geometric Structure Aided Visual Inertial Localization
- Authors: Huaiyang Huang, Haoyang Ye, Jianhao Jiao, Yuxiang Sun, Ming Liu
- Abstract summary: We present a complete visual localization system based on a hybrid map representation to reduce the computational cost and increase the positioning accuracy.
For batch optimization, instead of using visual factors, we develop a module to estimate a pose prior to the instant localization results.
The experimental results on the EuRoC MAV dataset demonstrate a competitive performance compared to the state of the arts.
- Score: 24.42071242531681
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Visual Localization is an essential component in autonomous navigation.
Existing approaches are either based on the visual structure from SLAM/SfM or
the geometric structure from dense mapping. To take the advantages of both, in
this work, we present a complete visual inertial localization system based on a
hybrid map representation to reduce the computational cost and increase the
positioning accuracy. Specially, we propose two modules for data association
and batch optimization, respectively. To this end, we develop an efficient data
association module to associate map components with local features, which takes
only $2$ms to generate temporal landmarks. For batch optimization, instead of
using visual factors, we develop a module to estimate a pose prior from the
instant localization results to constrain poses. The experimental results on
the EuRoC MAV dataset demonstrate a competitive performance compared to the
state of the arts. Specially, our system achieves an average position error in
1.7 cm with 100% recall. The timings show that the proposed modules reduce the
computational cost by 20-30%. We will make our implementation open source at
http://github.com/hyhuang1995/gmmloc.
Related papers
- SF-Loc: A Visual Mapping and Geo-Localization System based on Sparse Visual Structure Frames [3.5047603107971397]
SF-Loc is a lightweight visual mapping and map-aided localization system.
In the mapping phase, multi-sensor dense bundle adjustment (MS-DBA) is applied to construct geo-referenced visual structure frames.
In the localization phase, coarse-to-fine vision-based localization is performed, in which multi-frame information and the map distribution are fully integrated.
arXiv Detail & Related papers (2024-12-02T13:51:58Z) - 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) - Magic ELF: Image Deraining Meets Association Learning and Transformer [63.761812092934576]
This paper aims to unify CNN and Transformer to take advantage of their learning merits for image deraining.
A novel multi-input attention module (MAM) is proposed to associate rain removal and background recovery.
Our proposed method (dubbed as ELF) outperforms the state-of-the-art approach (MPRNet) by 0.25 dB on average.
arXiv Detail & Related papers (2022-07-21T12:50:54Z) - Structure PLP-SLAM: Efficient Sparse Mapping and Localization using
Point, Line and Plane for Monocular, RGB-D and Stereo Cameras [13.693353009049773]
This paper demonstrates a visual SLAM system that utilizes point and line cloud for robust camera localization, simultaneously, with an embedded piece-wise planar reconstruction (PPR) module.
We address the challenge of reconstructing geometric primitives with scale ambiguity by proposing several run-time optimizations on the reconstructed lines and planes.
The results show that our proposed SLAM tightly incorporates the semantic features to boost both tracking as well as backend optimization.
arXiv Detail & Related papers (2022-07-13T09:05:35Z) - Learning Implicit Feature Alignment Function for Semantic Segmentation [51.36809814890326]
Implicit Feature Alignment function (IFA) is inspired by the rapidly expanding topic of implicit neural representations.
We show that IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions.
Our method can be combined with improvement on various architectures, and it achieves state-of-the-art accuracy trade-off on common benchmarks.
arXiv Detail & Related papers (2022-06-17T09:40:14Z) - FD-SLAM: 3-D Reconstruction Using Features and Dense Matching [18.577229381683434]
We propose an RGB-D SLAM system that uses dense frame-to-model odometry to build accurate sub-maps.
We incorporate a learning-based loop closure component based on 3-D features which further stabilises map building.
The approach can also scale to large scenes where other systems often fail.
arXiv Detail & Related papers (2022-03-25T18:58:46Z) - Rethinking Dilated Convolution for Real-time Semantic Segmentation [0.0]
We take a different approach by using dilated convolutions with large dilation rates throughout the backbone.
Our model RegSeg achieves competitive results on real-time Cityscapes and CamVid datasets.
arXiv Detail & Related papers (2021-11-18T22:08:21Z) - Region Similarity Representation Learning [94.88055458257081]
Region Similarity Representation Learning (ReSim) is a new approach to self-supervised representation learning for localization-based tasks.
ReSim learns both regional representations for localization as well as semantic image-level representations.
We show how ReSim learns representations which significantly improve the localization and classification performance compared to a competitive MoCo-v2 baseline.
arXiv Detail & Related papers (2021-03-24T00:42:37Z) - GMMLoc: Structure Consistent Visual Localization with Gaussian Mixture
Models [23.72910988500612]
We present a method that tracks a camera in a prior map modelled by the Gaussian Mixture Model (GMM)
With the pose estimated by the front-end initially, the local visual observations and map components are associated efficiently.
We show how our system can provide a centimeter-level localization accuracy with only trivial computational overhead.
arXiv Detail & Related papers (2020-06-24T12:41:03Z) - TAM: Temporal Adaptive Module for Video Recognition [60.83208364110288]
temporal adaptive module (bf TAM) generates video-specific temporal kernels based on its own feature map.
Experiments on Kinetics-400 and Something-Something datasets demonstrate that our TAM outperforms other temporal modeling methods consistently.
arXiv Detail & Related papers (2020-05-14T08:22:45Z) - Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation [90.28365183660438]
This paper proposes an augmented parallel-pyramid net with attention partial module and differentiable auto-data augmentation.
We define a new pose search space where the sequences of data augmentations are formulated as a trainable and operational CNN component.
Notably, our method achieves the top-1 accuracy on the challenging COCO keypoint benchmark and the state-of-the-art results on the MPII datasets.
arXiv Detail & Related papers (2020-03-17T03:52:17Z)
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.