<p>For many years, terrain navigation has been successfully used in military airborne applications. Terrain navigation can essentially improve the performance of traditional inertial-based navigation. The latter is typically built around gyros and accelerometers, measuring the kinetic state changes. Although inertial-based systems benefit from their high independence, they, unfortunately, suffer from increasing error-growth due to accumulation of continuous measurement errors. </p><p>Undersea, the number of options for navigation support is fairly limited. Still, the navigation accuracy demands on autonomous underwater vehicles are increasing. For many military applications, surfacing to receive a GPS position- update is not an option. Lately, some attention has, instead, shifted towards terrain aided navigation. </p><p>One fundamental aim of this work has been to show what can be done within the field of terrain aided underwater navigation, using relatively simple means. A concept has been built around a narrow-beam altimeter, measuring the depth directly beneath the vehicle as it moves ahead. To estimate the vehicle location, based on the depth measurements, a particle filter algorithm has been implemented. A number of MATLAB simulations have given a qualitative evaluation of the chosen algorithm. In order to acquire data from actual underwater terrain, a small area of the Swedish lake, Lake Vättern has been charted. Results from simulations made on this data strongly indicate that the particle filter performs surprisingly well, also within areas containing relatively modest terrain variation.</p>

For many years, terrain navigation has been successfully used in military airborne applications. Terrain navigation can essentially improve the performance of traditional inertial-based navigation. The latter is typically built around gyros and accelerometers, measuring the kinetic state changes. Although inertial-based systems benefit from their high independence, they, unfortunately, suffer from increasing error-growth due to accumulation of continuous measurement errors. Undersea, the number of options for navigation support is fairly limited. Still, the navigation accuracy demands on autonomous underwater vehicles are increasing. For many military applications, surfacing to receive a GPS position- update is not an option. Lately, some attention has, instead, shifted towards terrain aided navigation. One fundamental aim of this work has been to show what can be done within the field of terrain aided underwater navigation, using relatively simple means. A concept has been built around a narrow-beam altimeter, measuring the depth directly beneath the vehicle as it moves ahead. To estimate the vehicle location, based on the depth measurements, a particle filter algorithm has been implemented. A number of MATLAB simulations have given a qualitative evaluation of the chosen algorithm. In order to acquire data from actual underwater terrain, a small area of the Swedish lake, Lake Vättern has been charted. Results from simulations made on this data strongly indicate that the particle filter performs surprisingly well, also within areas containing relatively modest terrain variation.

Autonomous relative terrain navigation is a problem at the forefront of many space missions involving close proximity operations to any target body. With no definitive answer, there are many techniques to help cope with this issue using both passive and active sensors, but almost all require high fidelity models of the associated dynamics in the environment. Convolutional Neural Networks (CNNs) trained with images rendered from a digital terrain map (DTM) of the body’s surface can provide a way to side-step the issue of unknown or complex dynamics while still providing reliable autonomous navigation. This is achieved by directly mapping an image to a relative position to the target body. The portability of trained CNNs allows “offline” training that can yield a matured network capable of being loaded onto a spacecraft for real-time position acquisition. In this thesis the lunar surface is used as the proving ground for this optical navigation technique, but the methods used are not unique to the Moon, and are applicable in general.

There are many applications for which small unmanned aerial vehicles (SUAVs) are well suited, including surveillance, reconnaissance, search and rescue, convoy support, and short-range low-altitude perimeter patrol missions. As technologies for microcontrollers and small sensors have improved, so have the capabilities of SUAVs. These improvements in SUAV performance increase the possibility for hazardous missions through mountainous and urban terrain in the successful completion of many of these missions. The focus of this research was on remote terrain navigation and the issues faced when dealing with limited onboard processing and limited payload and power capabilities. Additional challenges associated with canyon and urban navigation missions included reactive path following, sensor noise, and flight test design and execution. The main challenge was for an SUAV to successfully navigate through a mountainous canyon by reactively altering its own preplanned path to avoid canyon walls and other stationary obstacles. A robust path following method for SUAVs that uses a vector field approach to track functionally curved paths is presented along with flight test results. In these results, the average tracking error for an SUAV following a variety of curved paths is 3.4~m for amplitudes ranging between 10 and 100~m and spatial periods between 125 and 500~m. Additionally, a reactive path following method is presented that allows a UAV to continually offset or bias its planned path as distance information from the left and right ranging sensors is computed. This allows the UAV to to center itself between potential hazards even with imperfect waypoint path planning. Flight results of an SUAV reactively navigating through mountainous canyons experimentally verify the feasibility of this approach. In a flight test through Goshen Canyon in central Utah, an SUAV biased its planned path by 3 to 10~m to the right as it flew to center itself through the canyon and avoid the possibility of crashing into a canyon wall.

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