Pedestrian navigation in outdoor environments where global navigation satellite systems (GNSS) are unavailable is a challenging problem. Existing technologies that have attempted to address this problem often require external reference signals or specialized hardware, the extra size, weight, power, and cost of which are unsuitable for many applications. This article presents a real-time, self-contained outdoor navigation application that uses only the existing sensors on a smartphone in conjunction with a preloaded digital elevation map. The core algorithm implements a particle filter, which fuses sensor data with a stochastic pedestrian motion model to predict the user’s position. The smartphone’s barometric elevation is then compared with the elevation map to constrain the position estimate. The system developed for this research was deployed on Android smartphones and tested in several terrains using a variety of elevation data sources. The results from these experiments show the system achieves positioning accuracies in the tens of meters that do not grow as a function of time.

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Sensor-equipped unoccupied aerial vehicles (UAVs) have the potential to help reduce search times and alleviate safety risks for first responders carrying out Wilderness Search and Rescue (WiSAR) operations, the process of finding and rescuing person(s) lost in wilderness areas. Unfortunately, visual sensors alone do not address the need for robustness across all the possible terrains, weather, and lighting conditions that WiSAR operations can be conducted in. The use of multi-modal sensors, specifically visual-thermal cameras, is critical in enabling WiSAR UAVs to perform in diverse operating conditions. However, due to the unique challenges posed by the wilderness context, existing dataset benchmarks are inadequate for developing vision-based algorithms for autonomous WiSAR UAVs. To this end, we present WiSARD, a dataset with roughly 56,000 labeled visual and thermal images collected from UAV flights in various terrains, seasons, weather, and lighting conditions. To the best of our knowledge, WiSARD is the first large-scale dataset collected with multi-modal sensors for autonomous WiSAR operations. We envision that our dataset will provide researchers with a diverse and challenging benchmark that can test the robustness of their algorithms when applied to real-world (life-saving) applications. Link to dataset: https://sites.google.com/uw.edu/wisard/ ⋆ Both authors contributed equally.