computer-vision-5

Indoor Localization

3260 papers • 126 benchmarks • 313 datasets

Indoor localization is a fundamental problem in indoor location-based applications.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in indoor-localization-5

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find indoor-localization-5 models and implementations

kyeongsoo/indoor_localization

2 papers 82

Datasets

Subtasks

No subtasks available.

Most implemented papers

Low-effort place recognition with WiFi fingerprints using deep learning

Michał R. Nowicki, Jan Wietrzykowski•Sun Nov 06 2016

This work proposes to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results, and shows that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification.

164

Content

0

Paper Graph

A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting

Kyeong Soo Kim, Sanghyuk Lee, Kaizhu Huang•Tue Dec 05 2017

The proposed scalable DNN architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting can achieve near state-of-the-art performance with just a single DNN and enables the implementation with lower complexity and energy consumption at mobile devices.

186 0

Paper Graph

InLoc: Indoor Visual Localization with Dense Matching and View Synthesis

M. Pollefeys, T. Pajdla, Josef Sivic, Mircea Cimpoi, Torsten Sattler, A. Torii, M. Okutomi, Hajime Taira•Thu Nov 07 2019

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor spaces. The method proceeds along three steps: (i) efficient retrieval of candidate poses that scales to large-scale environments, (ii) pose estimation using dense matching rather than sparse local features to deal with weakly textured indoor scenes, and (iii) pose verification by virtual view synthesis that is robust to significant changes in viewpoint, scene layout, and occlusion. Second, we release a new dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. Third, we demonstrate that our method significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data. Code and data are publicly available.

518 0

Paper Graph

Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free

Aoran Xiao, Yujin Chen, Ruizhi Chen, Mengyun Liu, Dewen Wu, Shuheng Zhao•Tue Jul 31 2018

A localization method based on image retrieval that can efficiently result in high location accuracy as well as orientation estimation and attempts to use lightweight datum to present the scene.

54 0

Paper Graph

Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network

Wolfram Burgard, Abhinav Valada, Rohit Mohan, Federico Boniardi, Tim Caselitz•Mon Mar 04 2019

A memory and computationally efficient monocular camera-based localization system that allows a robot to estimate its pose given an architectural floor plan and estimates the robot pose using a particle filter that matches the extracted edges to the given floor plan is proposed.

60 0

Paper Graph

Can WiFi Estimate Person Pose?

Fei Wang, Stanislav Panev, Jinsong Han, Ziyi Dai, Dong Huang•Fri Mar 29 2019

A fully convolutional network (FCN) is proposed, termed WiSPPN, to estimate single person pose from the collected data and annotations and replies to the natural question: can WiFi devices work like cameras for vision applications?

63 0

Paper Graph

D2-Net: A Trainable CNN for Joint Description and Detection of Local Features

M. Pollefeys, T. Pajdla, Josef Sivic, Torsten Sattler, A. Torii, Ignacio Rocco, Mihai Dusmanu•Wed May 08 2019

This work proposes an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector, and shows that this model can be trained using pixel correspondences extracted from readily available large-scale SfM reconstructions, without any further annotations.

836 0

Paper Graph

Joint Activity Recognition and Indoor Localization With WiFi Fingerprints

Fei Wang, Jianwei Feng, Yinliang Zhao, Xiaobin Zhang, Shiyuan Zhang, Jinsong Han•Tue Apr 09 2019

This paper proposes a novel deep learning framework for joint activity recognition and indoor localization task using WiFi channel state information (CSI) fingerprints, and develops a system running standard IEEE 802.11n WiFi protocol and collects more than 1400 CSI fingerprints.

187 0

Paper Graph

Adding a benchmark result helps the community track progress.