Vision-based Indoor Localization Research Articles

Design of vision‐based indoor positioning based on embedded system

Indoor positioning techniques have become very important in recent years. Due to the wide deployment of surveillance cameras, it has become feasible to use the videos for indoor positioning. The success of using this approach can also reduce the load of security persons of watching the monitors all the time. In this study, the authors propose a vision-based indoor positioning system. The proposed method uses a frame processing technique and applies the Gaussian mixture learning for video background model. The foreground object can be extracted by using the background subtraction. Based on the foreground object, the objects can be tracked and used in the direct linear transform, and generate a bird's-eye map with camera information. A real-time demonstration has been also provided. It shows the tracing of the moving objects and the bird's-eye view.

Vision-based Indoor Localization Algorithm using Improved ResNet

The output of the residual network fluctuates greatly with the change of the weight parameters, which greatly affects the performance of the residual network. For dealing with this problem, an improved residual network is proposed. Based on the classical residual network, batch normalization, adaptive -dropout random deactivation function and a new loss function are added into the proposed model. Batch normalization is applied to avoid vanishing/exploding gradients. -dropout is applied to increase the stability of the model, which we select different dropout method adaptively by adjusting parameter. The new loss function is composed by cross entropy loss function and center loss function to enhance the inter class dispersion and intra class aggregation. The proposed model is applied to the indoor positioning of mobile robot in the factory environment. The experimental results show that the algorithm can achieve high indoor positioning accuracy under the premise of small training dataset. In the real-time positioning experiment, the accuracy can reach 95.37.

VISION-BASED INDOOR LOCALIZATION VIA A VISUAL SLAM APPROACH

Abstract. With an increasing interest in indoor location based services, vision-based indoor localization techniques have attracted many attentions from both academia and industry. Inspired by the development of simultaneous localization and mapping technique (SLAM), we present a visual SLAM-based approach to achieve a 6 degrees of freedom (DoF) pose in indoor environment. Firstly, the indoor scene is explored by a keyframe-based global mapping technique, which generates a database from a sequence of images covering the entire scene. After the exploration, a feature vocabulary tree is trained for accelerating feature matching in the image retrieval phase, and the spatial structures obtained from the keyframes are stored. Instead of querying by a single image, a short sequence of images in the query site are used to extract both features and their relative poses, which is a local visual SLAM procedure. The relative poses of query images provide a pose graph-based geometric constraint which is used to assess the validity of image retrieval results. The final positioning result is obtained by selecting the pose of the first correct corresponding image.

Vision based Indoor Localization Method via Convolution Neural Network

Existing indoor localization methods have bottleneck constraints such as multipath effect for Wi-Fi based methods, high cost for ultra-wide-band based methods and poor anti-interference for Bluetooth-based methods and so on. In order to avoid these problems, a vision-based indoor localization method is proposed. Firstly, the whole deployment environment is departed into several regions and each region is assigned to a location center. Then, in offline mode, the VGG16NET is pre-trained by ImageNet dataset and it is fine-tuned by images on a custom dataset towards indoor localization. In online mode, the fully trained and converged VGG16NET takes as input a video stream captured by the front RGB camera of a mobile robot and outputs features specific to the current location. The features are then used as input to an ArcFace classifier which outputs the current location of the mobile robot. Experimental results show that our method can estimate the location of a mobile object with imaging capability accurately in cluttered unstructured scenes without any other additional device. The localization accuracy can reach to 94.7%.

MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a Museum

In recent years there has been a growing interest in the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discover and follow the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present Museum Assistant (MusA), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these applications.

Seeing Eye Phone: a smart phone-based indoor localization and guidance system for the visually impaired

In order to help the visually impaired as they navigate unfamiliar environment such as public buildings, this paper presents a novel smart phone, vision-based indoor localization, and guidance system, called Seeing Eye Phone. This system requires a smart phone from the user and a server. The smart phone captures and transmits images of the user facing forward to the server. The server processes the phone images to detect and describe 2D features by SURF and then matches them to the 2D features of the stored map images that include their corresponding 3D information of the building. After features are matched, Direct Linear Transform runs on a subset of correspondences to find a rough initial pose estimate and the Levenberg---Marquardt algorithm further refines the pose estimate to find a more optimal solution. With the estimated pose and the camera's intrinsic parameters, the location and orientation of the user are calculated using 3D location correspondence data stored for features of each image. Positional information is then transmitted back to the smart phone and communicated to the user via text-to-speech. This indoor guiding system uses efficient algorithms such as SURF, homographs, multi-view geometry, and 3D to 2D reprojection to solve a very unique problem that will benefit the visually impaired. The experimental results demonstrate the feasibility of using a simple machine vision system design to accomplish a complex task and the potential of building a commercial product based on this design.

Vision-Based Indoor Localization Using Artificial Landmarks and Natural Features on the Ceiling with Optical Flow and a Kalman Filter

This paper proposes a vision-based indoor localization method for autonomous vehicles. A single upward-facing digital camera was mounted on an autonomous vehicle and used as a vision sensor to identify artificial landmarks and any natural corner features. An interest point detector was used to find the natural features. Using an optical flow detection algorithm, information related to the direction and vehicle translation was defined. This information was used to track the vehicle movements. Random noise related to uneven light disrupted the calculation of the vehicle translation. Thus, to estimate the vehicle translation, a Kalman filter was used to calculate the vehicle position. These algorithms were tested on a vehicle in a real environment. The image processing method could recognize the landmarks precisely, while the Kalman filter algorithm could estimate the vehicle’s position accurately. The experimental results confirmed that the proposed approaches can be implemented in practical situations.

A MapReduce-based indoor visual localization system using affine invariant features

This paper proposes a vision-based indoor localization service system that adopts affine scale invariant features (ASIFT) in MapReduce framework. Compared to prior vision-based localization methods that use scale invariant features or bag-of-words to match database images, the proposed system with ASIFT achieves better localization hit rate, especially when the query image has a large viewing angle difference to the most similar database image. The heavy computation imposed by ASIFT feature detection and image registration is handled by processes designed in MapReduce framework to speed up the localization service. Experiments using a Hadoop computation cluster provide results that show the performance of the localization system. The better localization hit rate is demonstrated by comparing the proposed approach to previous work based on scale invariant feature matching and visual vocabulary.

Vision-Based Indoor Localization for Unmanned Aerial Vehicles

Small unmanned aerial vehicles are cost-effective and easy to operate, and especially suitable in dangerous indoor environments. However, because GPS is not available in an indoor environment, indoor localization is a crucial problem in developing small unmanned aerial vehicles (UAVs). This paper suggests vision-based indoor localization for UAVs in GPS-denied environments. Our approach is based on image matching by applying the scale invariant feature transform algorithm.