Navigation assistance for visually impaired (NAVI) refers to systems that are able to assist or guide people with vision loss, ranging from partially sighted to totally blind, by means of sound commands. In this paper, a new system for NAVI is presented based on visual and range information. Instead of using several sensors, we choose one device, a consumer RGB-D camera, and take advantage of both range and visual information. In particular, the main contribution is the combination of depth information with image intensities, resulting in the robust expansion of the range-based floor segmentation. On one hand, depth information, which is reliable but limited to a short range, is enhanced with the long-range visual information. On the other hand, the difficult and prone-to-error image processing is eased and improved with depth information. The proposed system detects and classifies the main structural elements of the scene providing the user with obstacle-free paths in order to navigate safely across unknown scenarios. The proposed system has been tested on a wide variety of scenarios and data sets, giving successful results and showing that the system is robust and works in challenging indoor environments.

Many robotic applications work with visual reference maps, which usually consist of sets of more or less organized images. In these applications, there is a compromise between the density of reference data stored and the capacity to identify later the robot localization, when it is not exactly in the same position as one of the reference views. Here we propose the use of a recently developed feature, SURF, to improve the performance of appearance-based localization methods that perform image retrieval in large data sets. This feature is integrated with a vision-based algorithm that allows both topological and metric localization using omnidirectional images in a hierarchical approach. It uses pyramidal kernels for the topological localization and three-view geometric constraints for the metric one. Experiments with several omnidirectional images sets are shown, including comparisons with other typically used features (radial lines and SIFT). The advantages of this approach are proved, showing the use of SURF as the best compromise between efficiency and accuracy in the results.

The problem of 3D layout recovery in indoor scenes has been a core research topic for over a decade. However, there are still several major challenges that remain unsolved. Among the most relevant ones, a major part of the state-of-the-art methods make implicit or explicit assumptions on the scenes -e.g. box-shaped or Manhattan layouts. Also, current methods are computationally expensive and not suitable for real-time applications like robot navigation and AR/VR. In this work we present CFL (Corners for Layout), the first end-to-end model that predicts layout corners for 3D layout recovery on 360° images. Our experimental results show that we outperform the state of the art, making less assumptions on the scene than other works, and with lower cost. We also show that our model generalizes better to camera position variations than conventional approaches by using EquiConvs, a convolution applied directly on the spherical projection and hence invariant to the equirectangular distortions.

Indoor localization systems are undoubtedly of interest in many application fields. Like outdoor systems, they suffer from non-line-of-sight (NLOS) errors which hinder their robustness and accuracy. Though many ad hoc techniques have been developed to deal with this problem, unfortunately most of them are not applicable indoors due to the high variability of the environment (movement of furniture and of people, etc.). In this paper, we describe the use of robust regression techniques to detect and reject NLOS measures in a location estimation using multilateration. We show how the least-median-of-squares technique can be used to overcome the effects of NLOS errors, even in environments with little infrastructure, and validate its suitability by comparing it to other methods described in the bibliography. We obtained remarkable results when using it in a real indoor positioning system that works with Bluetooth and ultrasound (BLUPS), even when nearly half the measures suffered from NLOS or other coarse errors.

In this paper, we present a visual servo controller that effects optimal paths for a nonholonomic differential drive robot with field-of-view constraints imposed by the vision system. The control scheme relies on the computation of homographies between current and goal images, but unlike previous homography-based methods, it does not use the homography to compute estimates of pose parameters. Instead, the control laws are directly expressed in terms of individual entries in the homography matrix. In particular, we develop individual control laws for the three path classes that define the language of optimal paths: rotations, straight-line segments, and logarithmic spirals. These control laws, as well as the switching conditions that define how to sequence path segments, are defined in terms of the entries of homography matrices. The selection of the corresponding control law requires the homography decomposition before starting the navigation. We provide a controllability and stability analysis for our system and give experimental results.