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
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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.