ARIANNA stands for pAth Recognition for Indoor Assisted Navigation with Augmented perception. It is a flexible and low cost navigation system for vi- sually impaired people. Arianna permits to navigate colored paths painted or sticked on the floor revealing their directions through vibrational feedback on commercial smartphones.
Several assistance tools exists to overcome visual impairments, most of them exploiting vocal instructions to inform the traveller about his position and the near environment. For example, virtual acoustic displays and verbal commands issued by a synthetic speech display are used in @cite_9 . AudioGPS @cite_4 and Melodious Walkabout @cite_1 use audio cues to provide information on the surrounding environment. Dead-reckoning techniques are employed in Navatar @cite_5 where users interact with the application and help correcting possible navigation errors. In @cite_16 , vibrational feed-back is given by a special glove in the Finger-Braille language. This system requires some dedicated hardware and is specific to the language used. RF-PATH-ID @cite_10 , instead, is based on disseminating passive RFID tags and using a dedicated reader to acquire information on the user location. More examples and detailed information on indoor localization techniques may be found in @cite_12 .
@cite_4 @cite_9 @cite_1 @cite_5 @cite_16 @cite_10 @cite_12
2118322861 2163314033 2017967298 2467931 2077693798 2951423096 2736621623 2043732461 2795911278 2115555252 2013503669 2784500886 2101648351 2054602086 1988268155 1982321556 2898008292 2206106412 1779294284 2016630033
Indoor navigation systems for users who are visually impaired typically rely upon expensive physical augmentation of the environment or expensive sensing equipment; consequently few systems have been implemented. We present an indoor navigation system called Navatar that allows for localization and navigation by exploiting the physical characteristics of indoor environments, taking advantage of the unique sensing abilities of users with visual impairments, and minimalistic sensing achievable with low cost accelerometers available in smartphones. Particle filters are used to estimate the user's location based on the accelerometer data as well as the user confirming the presence of anticipated tactile landmarks along the provided path. Navatar has a high possibility of large-scale deployment, as it only requires an annotated virtual representation of an indoor environment. A user study with six blind users determines the accuracy of the approach, collects qualitative experiences and identifies areas for improvement. In this paper, we discuss application possibilities of augmented reality technologies in the field of mobility support for the deaf blind. We propose the navigation system called virtual leading blocks for the deaf-blind, which consists of a wearable interface for Finger-Braille, one of the commonly used communication methods among deaf-blind people in Japan, and a ubiquitous environment for barrier-free application, which consists of floor-embedded active radio-frequency identification (RFID) tags. The wearable Finger-Braille interface using two Linux-based wristwatch computers has been developed as a hybrid interface of verbal and nonverbal communication in order to inform users of their direction and position through the tactile sensation. We propose the metaphor of "watermelon splitting" for navigation by this system and verify the feasibility of the proposed system through experiments. The research we are reporting here is part of our effort to develop a navigation system for the blind. Our long-term goal is to create a portable, self-contained system that will allow visually impaired individuals to travel through familiar and unfamiliar environments without the assistance of guides. The system, as it exists now, consists of the following functional components: (1) a module for determining the traveler's position and orientation in space, (2) a Geographic Information System comprising a detailed database of our test site and software for route planning and for obtaining information from the database, and (3) the user interface. The experiment reported here is concerned with one function of the navigation system: guiding the traveler along a predefined route. We evaluate guidance performance as a function of four different display modes: one involving spatialized sound from a virtual acoustic display, and three involving verbal commands issued by a synthetic speech display. The virtual display mode fared best in terms of both guidance performance and user preferences. In this paper the design, development and evaluation of a GPS-based auditory navigation system is presented that implicitly guides a user by a contextualized rendering of personal audio files. The benefit of this navigation system is that the user can listen to his own audio contents while being navigated. Wearing headphones, the user listens to audio contents which are located in a virtual environment. The user simply walks in the direction where the sound seems to have its origin. A formal evaluation under field conditions proved that navigation with contextualized audio contents is efficient and intuitive and that users are highly satisfied with the navigation support given by the evaluated auditory display. In this paper we describe a prototype spatial audio user interface for a Global Positioning System (GPS). The interface is designed to allow mobile users to carry out location tasks while their eyes, hands or attention are otherwise engaged. Audio user interfaces for GPS have typically been designed to meet the needs of visually impaired users, and generally, though not exclusively, employ speech-audio. In contrast, our prototype system uses a simple form of non-speech, spatial audio. This paper analyses various candidate audio mappings for location and distance information. A variety of tasks, design considerations, design trade-offs and opportunities are considered. The findings from pilot empirical testing are reported. Finally, opportunities for improvements to the system and for future evaluation are explored. A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetically discriminative speaker discriminative DNNs as feature extractors for speaker verification has shown promising results. The extracted frame-level (DNN bottleneck, posterior or d-vector) features are equally weighted and aggregated to compute an utterance-level speaker representation (d-vector or i-vector). In this work we use speaker discriminative CNNs to extract the noise-robust frame-level features. These features are smartly combined to form an utterance-level speaker vector through an attention mechanism. The proposed attention model takes the speaker discriminative information and the phonetic information to learn the weights. The whole system, including the CNN and attention model, is joint optimized using an end-to-end criterion. The training algorithm imitates exactly the evaluation process --- directly mapping a test utterance and a few target speaker utterances into a single verification score. The algorithm can automatically select the most similar impostor for each target speaker to train the network. We demonstrated the effectiveness of the proposed end-to-end system on Windows @math "Hey Cortana" speaker verification task. Our work builds upon Visual Teach & Repeat 2 (VT&R2): a vision-in-the-loop autonomous navigation system that enables the rapid construction of route networks, safely built through operator-controlled driving. Added routes can be followed autonomously using visual localization. To enable long-term operation that is robust to appearance change, its Multi-Experience Localization (MEL) leverages many previously driven experiences when localizing to the manually taught network. While this multi-experience method is effective across appearance change, the computation becomes intractable as the number of experiences grows into the tens and hundreds. This paper introduces an algorithm that prioritizes experiences most relevant to live operation, limiting the number of experiences required for localization. The proposed algorithm uses a visual Bag-of-Words description of the live view to select relevant experiences based on what the vehicle is seeing right now, without having to factor in all possible environmental influences on scene appearance. This system runs in the loop, in real time, does not require bootstrapping, can be applied to any pointfeature MEL paradigm, and eliminates the need for visual training using an online, local visual vocabulary. By picking a subset of visually similar experiences to the live view, we demonstrate safe, vision-in-the-loop route following over a 31 hour period, despite appearance as different as night and day. In this paper we present a prototype system for image based localization in urban environments. Given a database of views of city street scenes tagged by GPS locations, the system computes the GPS location of a novel query view. We first use a wide-baseline matching technique based on SIFT features to select the closest views in the database. Often due to a large change of viewpoint and presence of repetitive structures, a large percentage of matches (> 50 ) are not correct correspondences. The subsequent motion estimation between the query view and the reference view, is then handled by a novel and efficient robust estimation technique capable of dealing with large percentage of outliers. This stage is also accompanied by a model selection step among the fundamental matrix and the homography. Once the motion between the closest reference views is estimated, the location of the query view is then obtained by triangulation of translation directions. Approximate solutions for cases when triangulation cannot be obtained reliably are also described. The presented system is tested on the dataset used in ICCV 2005 Computer Vision Contest and is shown to have higher accuracy than previous reported results. Navigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental interest in the study and development of artificial intelligence. Long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by recognisable landmarks and robust visual processing, that can simultaneously support continuous self-localisation ("I am here") and a representation of the goal ("I am going there"). Building upon recent research that applies deep reinforcement learning to maze navigation problems, we present an end-to-end deep reinforcement learning approach that can be applied on a city scale. Recognising that successful navigation relies on integration of general policies with locale-specific knowledge, we propose a dual pathway architecture that allows locale-specific features to be encapsulated, while still enabling transfer to multiple cities. We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away. The project webpage this http URL contains a video summarising our research and showing the trained agent in diverse city environments and on the transfer task, the form to request the StreetLearn dataset and links to further resources. The StreetLearn environment code is available at this https URL This paper presents results demonstrating real-time six degree of freedom localization, mapping, navigation and obstacle avoidance in an outdoor environment using only a lowcost o-the-shelf inertial measurement unit and a monocular camera. This navigation system is intended for operation of small unmanned aerial vehicles when GPS signals are unavailable due to obstructions or jamming and when operating in cluttered environments such as urban canyons or forests. A small radio-controlled car is used as a test bed. A bearings-only Simultaneous Localization and Mapping algorithm was implemented to localize both the vehicle and obstacles. This paper describes: (a) the hardware used; (b) a two-step approach for data association (image frame to image frame followed by image frame to map); (c) a technique for landmark initialization for the case where landmarks are located on the ground. Hardware test results demonstrating navigation to a goal in an obstacle strewn environment are presented. The eect of unmodelled sensor biases is examined in simulation. Recent years have seen the advent of new RF-localization systems that demonstrate tens of centimeters of accuracy. However, such systems require either deployment of new infrastructure, or extensive fingerprinting of the environment through training or crowdsourcing, impeding their wide-scale adoption. We present Ubicarse, an accurate indoor localization system for commodity mobile devices, with no specialized infrastructure or fingerprinting. Ubicarse enables handheld devices to emulate large antenna arrays using a new formulation of Synthetic Aperture Radar (SAR). Past work on SAR requires measuring mechanically controlled device movement with millimeter precision, far beyond what commercial accelerometers can provide. In contrast, Ubicarse's core contribution is the ability to perform SAR on handheld devices twisted by their users along unknown paths. Ubicarse is not limited to localizing RF devices; it combines RF localization with stereo-vision algorithms to localize common objects with no RF source attached to them. We implement Ubicarse on a HP SplitX2 tablet and empirically demonstrate a median error of 39 cm in 3-D device localization and 17 cm in object geotagging in complex indoor settings. ASR (automatic speech recognition) systems like Siri, Alexa, Google Voice or Cortana has become quite popular recently. One of the key techniques enabling the practical use of such systems in people's daily life is deep learning. Though deep learning in computer vision is known to be vulnerable to adversarial perturbations, little is known whether such perturbations are still valid on the practical speech recognition. In this paper, we not only demonstrate such attacks can happen in reality, but also show that the attacks can be systematically conducted. To minimize users' attention, we choose to embed the voice commands into a song, called CommandSong. In this way, the song carrying the command can spread through radio, TV or even any media player installed in the portable devices like smartphones, potentially impacting millions of users in long distance. In particular, we overcome two major challenges: minimizing the revision of a song in the process of embedding commands, and letting the CommandSong spread through the air without losing the voice "command". Our evaluation demonstrates that we can craft random songs to "carry" any commands and the modify is extremely difficult to be noticed. Specially, the physical attack that we play the CommandSongs over the air and record them can success with 94 percentage. In this paper, we describe a system that can carry out simultaneous localization and mapping (SLAM) in large indoor and outdoor environments using a stereo pair moving with 6 DOF as the only sensor. Unlike current visual SLAM systems that use either bearing-only monocular information or 3-D stereo information, our system accommodates both monocular and stereo. Textured point features are extracted from the images and stored as 3-D points if seen in both images with sufficient disparity, or stored as inverse depth points otherwise. This allows the system to map both near and far features: the first provide distance and orientation, and the second provide orientation information. Unlike other vision-only SLAM systems, stereo does not suffer from ldquoscale driftrdquo because of unobservability problems, and thus, no other information such as gyroscopes or accelerometers is required in our system. Our SLAM algorithm generates sequences of conditionally independent local maps that can share information related to the camera motion and common features being tracked. The system computes the full map using the novel conditionally independent divide and conquer algorithm, which allows constant time operation most of the time, with linear time updates to compute the full map. To demonstrate the robustness and scalability of our system, we show experimental results in indoor and outdoor urban environments of 210 m and 140 m loop trajectories, with the stereo camera being carried in hand by a person walking at normal walking speeds of 4--5 km h. We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that "sense" these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1:69m. War-driving is not necessary, neither are floorplans the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment. In this paper, we present an approach to visual SLAM and human tracking for a wheelchair robot equipped with a Microsoft Kinect sensor that which is a novel sensing system that captures RGB and depth (RGB-D) images simultaneously. The speeded-up robust feature (SURF) algorithm is employed to provide the robust description of feature for environments and the target person from RGB images. Based on the environmental SURF features, we present the natural landmark based simultaneous localization and mapping with the extended Kalman filter suing RGB-D data. Meanwhile, a depth clustering based human detection is proposed to extract human candidates. Accordantly, the target person tracking is achieved with an online learned RGB-D appearance model by integrating histogram orientation of gradient descriptor, color, depth, and position information from the body of the identified caregiver. Moreover, a fuzzy based controller provides dynamical human following for the wheelchair robot with a desired interval. Consequently, the experimental results demonstrated the effectiveness and feasibility in real world environments. With the popularization of mobile devices, recent years have witnessed an emerging potential for mobile landmark search. In this scenario, the user experience heavily depends on the efficiency of query transmission over a wireless link. As sending a query photo is time consuming, recent works have proposed to extract compact visual descriptors directly on the mobile end towards low bit rate transmission. Typically, these descriptors are extracted based solely on the visual content of a query, and the location cues from the mobile end are rarely exploited. In this paper, we present a Location Discriminative Vocabulary Coding (LDVC) scheme, which achieves extremely low bit rate query transmission, discriminative landmark description, as well as scalable descriptor delivery in a unified framework. Our first contribution is a compact and location discriminative visual landmark descriptor, which is offline learnt in two-step: First, we adopt spectral clustering to segment a city map into distinct geographical regions, where both visual and geographical similarities are fused to optimize the partition of city-scale geo-tagged photos. Second, we propose to learn LDVC in each region with two schemes: (1) a Ranking Sensitive PCA and (2) a Ranking Sensitive Vocabulary Boosting. Both schemes embed location cues to learn a compact descriptor, which minimizes the retrieval ranking loss by replacing the original high-dimensional signatures. Our second contribution is a location aware online vocabulary adaption: We store a single vocabulary in the mobile end, which is efficiently adapted for a region specific LDVC coding once a mobile device enters a given region. The learnt LDVC landmark descriptor is extremely compact (typically 10---50 bits with arithmetical coding) and performs superior over state-of-the-art descriptors. We implemented the framework in a real-world mobile landmark search prototype, which is validated in a million-scale landmark database covering typical areas e.g. Beijing, New York City, Lhasa, Singapore, and Florence. Remote sensing image retrieval has many important applications in civilian and military fields, such as disaster monitoring and target detecting. However, the existing research on image retrieval, mainly including to two directions, text based and content based, cannot meet the rapid and convenient needs of some special applications and emergency scenes. Based on text, the retrieval is limited by keyboard inputting because of its lower efficiency for some urgent situations and based on content, it needs an example image as reference, which usually does not exist. Yet speech, as a direct, natural and efficient human-machine interactive way, can make up these shortcomings. Hence, a novel cross-modal retrieval method for remote sensing image and spoken audio is proposed in this paper. We first build a large-scale remote sensing image dataset with plenty of manual annotated spoken audio captions for the cross-modal retrieval task. Then a Deep Visual-Audio Network is designed to directly learn the correspondence of image and audio. And this model integrates feature extracting and multi-modal learning into the same network. Experiments on the proposed dataset verify the effectiveness of our approach and prove that it is feasible for speech-to-image retrieval. In this paper, we explore the use of synthesized landmark maps for absolute localization of a smart wheelchair system outdoors. In this paradigm, three-dimensional map data are acquired by an automobile equipped with high precision inertial GPS systems, in conjunction with light detection and ranging (LIDAR) systems, whose range measurements are subsequently registered to a global coordinate frame. The resulting map data are then synthesized a priori to identify robust, salient features for use as landmarks in localization. By leveraging such maps with landmark meta-data, robots possessing far lower cost sensor suites gain many of the benefits obtained from the higher fidelity sensors, but without the cost.We show that by using such a map-based localization approach, a smart wheelchair system outfitted only with a 2-D LIDAR and encoders was able to maintain accurate, global pose estimates outdoors over almost 1 km paths. This paper proposes a deep bidirectional long short-term memory approach in modeling the long contextual, nonlinear mapping between audio and visual streams for video-realistic talking head. In training stage, an audio-visual stereo database is firstly recorded as a subject talking to a camera. The audio streams are converted into acoustic feature, i.e. Mel-Frequency Cepstrum Coefficients (MFCCs), and their textual labels are also extracted. The visual streams, in particular, the lower face region, are compactly represented by active appearance model (AAM) parameters by which the shape and texture variations can be jointly modeled. Given pairs of the audio and visual parameter sequence, a DBLSTM model is trained to learn the sequence mapping from audio to visual space. For any unseen speech audio, whether it is original recorded or synthesized by text-to-speech (TTS), the trained DBLSTM model can predict a convincing AAM parameter trajectory for the lower face animation. To further improve the realism of the proposed talking head, the trajectory tiling method is adopted to use the DBLSTM predicted AAM trajectory as a guide to select a smooth real sample image sequence from the recorded database. We then stitch the selected lower face image sequence back to a background face video of the same subject, resulting in a video-realistic talking head. Experimental results show that the proposed DBLSTM approach outperforms the existing HMM-based approach in both objective and subjective evaluations. This paper describes a system and set of algorithms for automatically inducing stand-alone monolingual part-of-speech taggers, base noun-phrase bracketers, named-entity taggers and morphological analyzers for an arbitrary foreign language. Case studies include French, Chinese, Czech and Spanish.Existing text analysis tools for English are applied to bilingual text corpora and their output projected onto the second language via statistically derived word alignments. Simple direct annotation projection is quite noisy, however, even with optimal alignments. Thus this paper presents noise-robust tagger, bracketer and lemmatizer training procedures capable of accurate system bootstrapping from noisy and incomplete initial projections.Performance of the induced stand-alone part-of-speech tagger applied to French achieves 96 core part-of-speech (POS) tag accuracy, and the corresponding induced noun-phrase bracketer exceeds 91 F-measure. The induced morphological analyzer achieves over 99 lemmatization accuracy on the complete French verbal system.This achievement is particularly noteworthy in that it required absolutely no hand-annotated training data in the given language, and virtually no language-specific knowledge or resources beyond raw text. Performance also significantly exceeds that obtained by direct annotation projection.