Ranging information from ultra-wideband (UWB) ranging radios can be used to improve estimated navigation accuracy of a ground robot with other on-board sensors. However, all ranging-aided navigation methods demand the locations of ranging nodes to be known, which is not suitable for time-pressed situations, dynamic cluttered environments, or collaborative navigation applications. This paper describes a new ranging-aided navigation approach that does not require the locations of ranging radios. Our approach formulates relative pose constraints using ranging readings. The formulation is based on geometric relationships between each stationary ranging node and two ranging antennas on the moving robot across time. Our experiments show that estimated navigation accuracy of the ground robot is substantially enhanced with ranging information using our approach under a variety of scenarios, when ranging nodes are placed at unknown locations. We analyze and compare our performance with a traditional ranging-aided method, which requires mapping the positions of ranging nodes. We also demonstrate the applicability of our approach for collaborative navigation in large-scale unknown environments, by using ranging information from one mobile robot to improve navigation estimation of the other robot. This application does not require the installation of ranging nodes at fixed locations.

AR-Mentor is a wearable real time Augmented Reality (AR) mentoring system that is configured to assist in maintenance and repair tasks of complex machinery, such as vehicles, appliances, and industrial machinery. The system combines a wearable Optical-See-Through (OST) display device with high precision 6-Degree-Of-Freedom (DOF) pose tracking and a virtual personal assistant (VPA) with natural language, verbal conversational interaction, providing guidance to the user in the form of visual, audio and locational cues. The system is designed to be heads-up and hands-free allowing the user to freely move about the maintenance or training environment and receive globally aligned and context aware visual and audio instructions (animations, symbolic icons, text, multimedia content, speech). The user can interact with the system, ask questions and get clarifications and specific guidance for the task at hand. A pilot application with AR-Mentor was successfully built to instruct a novice to perform an advanced 33-step maintenance task on a training vehicle. The initial live training tests demonstrate that AR-Mentor is able to help and serve as an assistant to an instructor, freeing him/her to cover more students and to focus on higher-order teaching.