CDI-Type II: Collaborative Research: Cyber Enhancement of Spatial Cognition for the Visually Impaired

Wayfinding is an essential capability for any person who wishes to have an independent life-style. It requires successful execution of several tasks including navigation and object and place recognition, all of which necessitate accurate assessment of the surrounding environment. For a visually-impaired person these tasks may be exceedingly difficult to accomplish and there are risks associated with failure in any of these. Guide dogs and white canes are widely used for the purpose of navigation and environment sensing, respectively. The former, however, has costly and often prohibitive training requirements, while the latter can only provide cues about obstacles in one\u27s surroundings. Human performance on visual information dependent tasks can be improved by sensing which provides information and environmental cues, such as position, orientation, local geometry, object description, via the use of appropriate sensors and sensor fusion algorithms. Most work on wayfinding aids has focused on outdoor environments and has led to the development of speech-enabled GPS-based navigation systems that provide information describing streets, addresses and points of interest. In contrast, the limited technology that is available for indoor navigation requires significant modification to the building infrastructure, whose high cost has prevented its wide use. This proposal adopts a multi-faceted approach for solving the indoor navigation problem for people with limited vision. It leverages expertise from robotics, computer vision, and blind spatial cognition with behavioral studies on interface design to guide the discovery of information requirements and optimal delivery methods for an indoor navigation system. Designing perception and navigation algorithms, implemented on miniature-size commercially-available hardware, while explicitly considering the spatial cognition capabilities of the visually impaired, will lead to the development of indoor navigation systems that will assist blind people in their wayfinding tasks while facilitating cognitive-map development

Development of a smartphone based indoor navigation system for visually impaired people

We have implemented an Android smartphone based system for localization and navigation in indoor environments of blind and visual impaired people. Through the reading of sensor data, we have created a dead reckoning system to estimate the user’s position as a function of the individuated number of steps and the orientation of its heading, to represent the path on a two-dimensional map, and to save/load the map in a persistent formope

Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return.

We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians. We present a new algorithm for path matching, and test it in simulated assisted return tasks with data from WeAllWalk, the only existing data set with inertial data recorded from blind walkers. We consider two odometry systems, one based on deep learning (RoNIN), and the second based on robust turn detection and step counting. Our results show that the best path matching results are obtained using the turns/steps odometry system

Using Wii technology to explore real spaces via virtual environments for people who are blind

Purpose - Virtual environments (VEs) that represent real spaces (RSs) give people who are blind the opportunity to build a cognitive map in advance that they will be able to use when arriving at the RS. Design - In this research study Nintendo Wii based technology was used for exploring VEs via the Wiici application. The Wiimote allows the user to interact with VEs by simulating walking and scanning the space. Finding - By getting haptic and auditory feedback the user learned to explore new spaces. We examined the participants' abilities to explore new simple and complex places, construct a cognitive map, and perform orientation tasks in the RS. Originality – To our knowledge, this finding presents the first virtual environment for people who are blind that allow the participants to scan the environment and by this to construct map model spatial representations

A multimodal smartphone interface for active perception by visually impaired

The diffuse availability of mobile devices, such as smartphones and tablets, has the potential to bring substantial benefits to the people with sensory impairments. The solution proposed in this paper is part of an ongoing effort to create an accurate obstacle and hazard detector for the visually impaired, which is embedded in a hand-held device. In particular, it presents a proof of concept for a multimodal interface to control the orientation of a smartphone's camera, while being held by a person, using a combination of vocal messages, 3D sounds and vibrations. The solution, which is to be evaluated experimentally by users, will enable further research in the area of active vision with human-in-the-loop, with potential application to mobile assistive devices for indoor navigation of visually impaired people

Design strategies in facades for the reduction of housing energy consumption

This article analyzes the energy-saving potential of various facade design strategies from a life cycle perspective, including the energy needed in the use stage and the embodied energy of materials. The results provide reference data on the behaviour of these systems in Spain and make it possible to identify the best strategies for reducing energy consumption in a wide variety of potential situations that may arise in both new construction and in the rehabilitation of existing facades. The impact categories studied are fossil fuel depletion and climate change, and design strategies are linked to climate data, orientation, air change rate, facade materials and wall composition. Exchanges between the interior and exterior environments take place through the building envelope, some of whose key design parameters include lighting, ventilation and heat flux. Improving this envelope can greatly reduce environmental impact, ensuring indoor environmental quality. This analysis confirms the need to consider the interactions among the parameters studied, as it shows that there are several design solutions with similar impacts, which can be adapted to project requirements. In both new construction and rehabilitation, some of these parameters may be determined by other design decisions not necessarily aimed at reducing environmental impact, so it can be very useful to be aware of a variety of design alternatives that can be implemented in specific projects

A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving

3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic calibration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed