We introduce a novel framework for assistive urban navigation for individuals with low vision. Utilizing a smart glasses platform developed by Biel Glasses, which provide a continuous stream of stereo images and GPS fixes, we generate an Event Map based on key semantic elements extracted by carefully prompted visual question-answering (VQA) models. For individuals with blurry or reduced fields of vision (low vision), traversing city streets poses a variety of challenges; they may struggle to perceive construction work, potholes, crowded sidewalks, and other ambiguous obstacles obstructing their paths. Some tasks, such as distinguishing traffic light signals, are nigh impossible without assistance from a companion or city infrastructure aimed towards accessibility. Although the majority of these problems may be solved with individually tailored traditional computer vision algorithms, developing and running a suite of these algorithms is challenging and resource demanding. Therefore, our proposed solution capitalizes on a single underlying implementation that need only be extended by adding queries. We validate our approach using a custom dataset of over 1,300 annotated images from various locations around Barcelona, reporting performance across different urban navigation tasks. We demonstrate the performance of the end to end system on a run of data collected by the Biel Glasses platform.

We present a novel initialization technique for the range-aided simultaneous localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements of point-to-point distances in addition to measurements of rigid transformations to landmark or pose variables. Standard formulations of RA-SLAM approach the problem as non-convex optimization, which requires a good initialization to obtain quality results. The initialization technique proposed here relaxes the RA-SLAM problem to a convex problem which is then solved to determine an initialization for the original, non-convex problem. The relaxation is a second-order cone program (SOCP), which is derived from a quadratically constrained quadratic program (QCQP) formulation of the RA-SLAM problem. As a SOCP, the method is highly scalable. We name this relaxation Second-order COnic RElaxation for RA-SLAM (SCORE). To our knowledge, this work represents the first convex relaxation for RA-SLAM. We present real-world and simulated experiments which show SCORE initialization permits the efficient recovery of quality solutions for a variety of challenging single- and multi-robot RA-SLAM problems with thousands of poses and range measurements.