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A critical use case of SLAM for mobile robots is to support localization during task-directed navigation. Current SLAM benchmarks overlook the importance of repeatability (precision) despite its impact on real-world deployments. TaskSLAM-Bench, a task-driven approach to SLAM benchmarking, addresses this gap. It employs precision as a key metric, accounts for SLAM’s mapping capabilities, and has easy-to-meet requirements. Simulated and real-world evaluation of SLAM methods provide insights into the navigation performance of modern visual and LiDAR SLAM solutions. The outcomes show that passive stereo SLAM precision may match that of 2D LiDAR SLAM in indoor environments. TaskSLAM-Bench complements existing benchmarks and offers richer assessment of SLAM performance in navigation-focused scenarios. Publicly available code permits in-situ SLAM testing in custom environments with properly equipped robots. 

The aim of this scoping review is to provide an overview of the types of AI-enabled Orientation and Mobility Technologies (AIOMTs) that have been developed for use by blind and visually impaired individuals and explore the multifaceted role of trust in its adoption and use. By synthesizing existing research, the review identifies the functional roles played by AIOMTs and key factors influencing trust, including system design, user interaction, and contextual considerations. Furthermore, this work examines how technology design, functional role, and trust impact the adoption, efficacy, and long-term engagement with AIOMTs. The findings provide a comprehensive framework for future research and development, emphasizing user-centered approaches and trust calibration mechanisms. 

Emerging trends in technology are providing opportunities for a broader range of accessible and assistive technologies (AATs) to positively impact persons with disabilities in terms of independent living and employment within and across built environments. However, such technologies typically require significant investments by entities that offer such options. It is not clear how such firms compete in a market with other firms that may not provide such options. Understanding such competition can help to promote greater investments in accessibility infrastructure within built environments by entities and provide insights into how federal efforts can further boost such efforts. To this end, this paper presents a game-theoretic framework of market competition between two firms where one invests in accessibility (bearing additional upfront costs) and compares it with another one that does not. Numerical evaluations demonstrate the range of parametric values where accessibility investments pay off. Furthermore, case studies are presented to demonstrate the practical feasibility of these parameter values. The results indicate that any firm considering making accessibility investments can expect to make profits and also gain an advantage over its competitors if the expected increase in the average user experience is significant (quantified as 20% or more for the parameters considered in this work) across all potential users. 

This paper extends the gap-based navigation technique Potential Gap with safety guarantees at the local planning level for a kinematic planar nonholonomic robot model, leading to Safer Gap . It relies on a subset of navigable free space from the robot to a gap, denoted the keyhole region. The region is defined by the union of the largest collision-free disc centered on the robot and a collision-free trapezoidal region directed through the gap. Safer Gap first generates Bézier-based collision-free paths within the keyhole regions. The keyhole region of the top scoring path is encoded by a shallow neural network-based zeroing barrier function (ZBF) synthesized in real-time. Nonlinear Model Predictive Control (NMPC) with Keyhole ZBF constraints and output tracking of the Bézier path, synthesizes a safe kinematically feasible trajectory. The Potential Gap projection operator serves as a last action to enforce safety if the NMPC optimization fails to converge to a solution within the prescribed time. Simulation and experimental validation of Safer Gap confirm its collision-free navigation properties.