Detección y modelado de escaleras con sensor RGB-D para asistencia personal

La habilidad de avanzar y moverse de manera efectiva por el entorno resulta natural para la mayoría de la gente, pero no resulta fácil de realizar bajo algunas circunstancias, como es el caso de las personas con problemas visuales o cuando nos movemos en entornos especialmente complejos o desconocidos. Lo que pretendemos conseguir a largo plazo es crear un sistema portable de asistencia aumentada para ayudar a quienes se enfrentan a esas circunstancias. Para ello nos podemos ayudar de cámaras, que se integran en el asistente. En este trabajo nos hemos centrado en el módulo de detección, dejando para otros trabajos el resto de módulos, como podría ser la interfaz entre la detección y el usuario. Un sistema de guiado de personas debe mantener al sujeto que lo utiliza apartado de peligros, pero también debería ser capaz de reconocer ciertas características del entorno para interactuar con ellas. En este trabajo resolvemos la detección de uno de los recursos más comunes que una persona puede tener que utilizar a lo largo de su vida diaria: las escaleras. Encontrar escaleras es doblemente beneficioso, puesto que no sólo permite evitar posibles caídas sino que ayuda a indicar al usuario la posibilidad de alcanzar otro piso en el edificio. Para conseguir esto hemos hecho uso de un sensor RGB-D, que irá situado en el pecho del sujeto, y que permite captar de manera simultánea y sincronizada información de color y profundidad de la escena. El algoritmo usa de manera ventajosa la captación de profundidad para encontrar el suelo y así orientar la escena de la manera que aparece ante el usuario. Posteriormente hay un proceso de segmentación y clasificación de la escena de la que obtenemos aquellos segmentos que se corresponden con "suelo", "paredes", "planos horizontales" y una clase residual, de la que todos los miembros son considerados "obstáculos". A continuación, el algoritmo de detección de escaleras determina si los planos horizontales son escalones que forman una escalera y los ordena jerárquicamente. En el caso de que se haya encontrado una escalera, el algoritmo de modelado nos proporciona toda la información de utilidad para el usuario: cómo esta posicionada con respecto a él, cuántos escalones se ven y cuáles son sus medidas aproximadas. En definitiva, lo que se presenta en este trabajo es un nuevo algoritmo de ayuda a la navegación humana en entornos de interior cuya mayor contribución es un algoritmo de detección y modelado de escaleras que determina toda la información de mayor relevancia para el sujeto. Se han realizado experimentos con grabaciones de vídeo en distintos entornos, consiguiendo buenos resultados tanto en precisión como en tiempo de respuesta. Además se ha realizado una comparación de nuestros resultados con los extraídos de otras publicaciones, demostrando que no sólo se consigue una eciencia que iguala al estado de la materia sino que también se aportan una serie de mejoras. Especialmente, nuestro algoritmo es el primero capaz de obtener las dimensiones de las escaleras incluso con obstáculos obstruyendo parcialmente la vista, como puede ser gente subiendo o bajando. Como resultado de este trabajo se ha elaborado una publicación aceptada en el Second Workshop on Assitive Computer Vision and Robotics del ECCV, cuya presentación tiene lugar el 12 de Septiembre de 2014 en Zúrich, Suiza

Indoor/outdoor navigation system based on possibilistic traversable area segmentation for visually impaired people

Autonomous collision avoidance for visually impaired people requires a specific processing for an accurate definition of traversable area. Processing of a real time image sequence for traversable area segmentation is quite mandatory. Low cost systems suggest use of poor quality cameras. However, real time low cost camera suffers from great variability of traversable area appearance at indoor as well as outdoor environments. Taking into account ambiguity affecting object and traversable area appearance induced by reflections, illumination variations, occlusions (, etc...), an accurate segmentation of traversable area in such conditions remains a challenge. Moreover, indoor and outdoor environments add additional variability to traversable areas. In this paper, we present a real-time approach for fast traversable area segmentation from image sequence recorded by a low-cost monocular camera for navigation system. Taking into account all kinds of variability in the image, we apply possibility theory for modeling information ambiguity. An efficient way of updating the traversable area model in each environment condition is to consider traversable area samples from the same processed image for building its possibility maps. Then fusing these maps allows making a fair model definition of the traversable area. Performance of the proposed system was evaluated on public databases, with indoor and outdoor environments. Experimental results show that this method is challenging leading to higher segmentation rates

SLAM for Visually Impaired People: A Survey

In recent decades, several assistive technologies for visually impaired and blind (VIB) people have been developed to improve their ability to navigate independently and safely. At the same time, simultaneous localization and mapping (SLAM) techniques have become sufficiently robust and efficient to be adopted in the development of assistive technologies. In this paper, we first report the results of an anonymous survey conducted with VIB people to understand their experience and needs; we focus on digital assistive technologies that help them with indoor and outdoor navigation. Then, we present a literature review of assistive technologies based on SLAM. We discuss proposed approaches and indicate their pros and cons. We conclude by presenting future opportunities and challenges in this domain.Comment: 26 pages, 5 tables, 3 figure

Enhancing perception for the visually impaired with deep learning techniques and low-cost wearable sensors

As estimated by the World Health Organization, there are millions of people who lives with some form of vision impairment. As a consequence, some of them present mobility problems in outdoor environments. With the aim of helping them, we propose in this work a system which is capable of delivering the position of potential obstacles in outdoor scenarios. Our approach is based on non-intrusive wearable devices and focuses also on being low-cost. First, a depth map of the scene is estimated from a color image, which provides 3D information of the environment. Then, an urban object detector is in charge of detecting the semantics of the objects in the scene. Finally, the three-dimensional and semantic data is summarized in a simpler representation of the potential obstacles the users have in front of them. This information is transmitted to the user through spoken or haptic feedback. Our system is able to run at about 3.8 fps and achieved a 87.99% mean accuracy in obstacle presence detection. Finally, we deployed our system in a pilot test which involved an actual person with vision impairment, who validated the effectiveness of our proposal for improving its navigation capabilities in outdoors.This work has been supported by the Spanish Government TIN2016-76515R Grant, supported with Feder funds, the University of Alicante project GRE16-19, and by the Valencian Government project GV/2018/022. Edmanuel Cruz is funded by a Panamenian grant for PhD studies IFARHU & SENACYT 270-2016-207. This work has also been supported by a Spanish grant for PhD studies ACIF/2017/243. Thanks also to Nvidia for the generous donation of a Titan Xp and a Quadro P6000

Non-Linearity Analysis of Depth and Angular Indexes for Optimal Stereo SLAM

In this article, we present a real-time 6DoF egomotion estimation system for indoor environments using a wide-angle stereo camera as the only sensor. The stereo camera is carried in hand by a person walking at normal walking speeds 3–5 km/h. We present the basis for a vision-based system that would assist the navigation of the visually impaired by either providing information about their current position and orientation or guiding them to their destination through different sensing modalities. Our sensor combines two different types of feature parametrization: inverse depth and 3D in order to provide orientation and depth information at the same time. Natural landmarks are extracted from the image and are stored as 3D or inverse depth points, depending on a depth threshold. This depth threshold is used for switching between both parametrizations and it is computed by means of a non-linearity analysis of the stereo sensor. Main steps of our system approach are presented as well as an analysis about the optimal way to calculate the depth threshold. At the moment each landmark is initialized, the normal of the patch surface is computed using the information of the stereo pair. In order to improve long-term tracking, a patch warping is done considering the normal vector information. Some experimental results under indoor environments and conclusions are presented

Portable Robotic Navigation Aid for the Visually Impaired

This dissertation aims to address the limitations of existing visual-inertial (VI) SLAM methods - lack of needed robustness and accuracy - for assistive navigation in a large indoor space. Several improvements are made to existing SLAM technology, and the improved methods are used to enable two robotic assistive devices, a robot cane, and a robotic object manipulation aid, for the visually impaired for assistive wayfinding and object detection/grasping. First, depth measurements are incorporated into the optimization process for device pose estimation to improve the success rate of VI SLAM\u27s initialization and reduce scale drift. The improved method, called depth-enhanced visual-inertial odometry (DVIO), initializes itself immediately as the environment\u27s metric scale can be derived from the depth data. Second, a hybrid PnP (perspective n-point) method is introduced for a more accurate estimation of the pose change between two camera frames by using the 3D data from both frames. Third, to implement DVIO on a smartphone with variable camera intrinsic parameters (CIP), a method called CIP-VMobile is devised to simultaneously estimate the intrinsic parameters and motion states of the camera. CIP-VMobile estimates in real time the CIP, which varies with the smartphone\u27s pose due to the camera\u27s optical image stabilization mechanism, resulting in more accurate device pose estimates. Various experiments are performed to validate the VI-SLAM methods with the two robotic assistive devices. Beyond these primary objectives, SM-SLAM is proposed as a potential extension for the existing SLAM methods in dynamic environments. This forward-looking exploration is premised on the potential that incorporating dynamic object detection capabilities in the front-end could improve SLAM\u27s overall accuracy and robustness. Various experiments have been conducted to validate the efficacy of this newly proposed method, using both public and self-collected datasets. The results obtained substantiate the viability of this innovation, leaving a deeper investigation for future work

VisPercep: A Vision-Language Approach to Enhance Visual Perception for People with Blindness and Low Vision

People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards on their own. In this paper, we present a pioneering approach that leverages a large vision-language model to enhance visual perception for pBLV, offering detailed and comprehensive descriptions of the surrounding environments and providing warnings about the potential risks. Our method begins by leveraging a large image tagging model (i.e., Recognize Anything (RAM)) to identify all common objects present in the captured images. The recognition results and user query are then integrated into a prompt, tailored specifically for pBLV using prompt engineering. By combining the prompt and input image, a large vision-language model (i.e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing the environmental objects and scenes, relevant to the prompt. We evaluate our approach through experiments conducted on both indoor and outdoor datasets. Our results demonstrate that our method is able to recognize objects accurately and provide insightful descriptions and analysis of the environment for pBLV