Autonomous multi-sensor integration for seamless indoor/outdoor navigation and mobile sensing

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The evolution of navigation and perception sensor technologies in recent years has opened the door for the development of low-cost autonomous systems, including autonomous mobile robots (AMR) and small unmanned aerial systems (UAS). Such low-cost systems can potentially be used in many critical applications, both outdoor and indoor. Outdoor applications include topographic mapping, disaster monitoring, precision agriculture, to name a few. Indoor applications, on the other hand, include manufacturing and distribution facilities, health sector, public safety, among others. The use of autonomous systems in these critical applications, however, requires continuous high-accuracy positioning and attitude (pose) information of the platform, along with environmental sensing and object classification. In addition, some use cases of market verticals such as smart manufacturing, warehousing and supply chain, and transportation require seamless indoor/outdoor operational capabilities. The proposed research presents a number of novel ideas, which collectively lead to the development of next generation algorithms and estimation methodologies for a low-cost autonomous multi-sensor integrated system for precise real-time seamless indoor/outdoor navigation and environmental sensing (NES). The proposed system takes advantage of a number of emerging low-cost sensors, including a multi-constellation global navigation satellite systems (GNSS) sensor, a micro-electro-mechanical system (MEMS)-based inertial sensor, a solid state LiDAR sensor, and digital and event cameras. It will also take advantage of emerging fifth-generation (5G) millimeter wave (mmWave) technology, which can help obtain high-accuracy localization in indoor and GNSS-challenging environments. Various levels of sensor/system integration will be developed for different indoor and outdoor environments using nonlinear filtering, deep learning and hybrid models. A software and a prototype system will be developed, which supports the operations of commercial next generation AMR and small UAS. The developed software can be used as a standalone product or it can be integrated with another system, for example, autonomous vehicle control. This in turn could lead to the development of a smart system that can automate the entire AMR or UAV mission. The proposed system overcomes the limitations of current NES systems and is expected to have significant economic and safety benefits to a number of critical industries. The proposed research will also help train a number of highly qualified personnel (HQP), who will be recruited from diverse backgrounds in a manner that ensures equity and inclusion. The HQP will be working collaboratively on the various components of the research project, including algorithm/software development, system design, data collection and system evaluation. Because of the importance of the proposed research, it is expected that the HQP will have a number of high-tech career opportunities.