It is common in pose graph optimization (PGO) algorithms to assume that noise in the translations and rotations of relative pose measurements is uncorrelated. However, existing work shows that in practice these measurements can be highly correlated, which leads to degradation in the accuracy of PGO solutions that rely on this assumption. Therefore, in this paper we develop a novel algorithm derived from a realistic, correlated model of relative pose uncertainty, and we quantify the resulting improvement in the accuracy of the solutions we obtain relative to state-of-the-art PGO algorithms. Our approach utilizes Riemannian optimization on the planar unit dual quaternion (PUDQ) manifold, and we prove that it converges to first-order stationary points of a Lie-theoretic maximum likelihood objective. Then we show experimentally that, compared to state-of-the-art PGO algorithms, this algorithm produces estimation errors that are lower by 10% to 25% across several orders of magnitude of correlated noise levels and graph sizes.

The partial-update filter is a Kalman filter modification that can accommodate higher nonlinearities and uncertainties than a nominal and Schmidt-Kalman filter. This robustness enhancement of the partial-update filter is attributed to its capability to limit the impact of incorrect updates by applying user-selected static percentages of the nominal Kalman update, to user-selected states at any time step.To further extend the partial-update capabilities and applicability, this paper presents two methods for dynamically and automatically selecting the partial-update percentages based on nonlinearity metrics of the process and measurement model. By enabling dynamic update percentages, the filter automatically leverages situations where higher updates can be applied and lower updates are deemed suitable. This leads to higher statistical consistency and accuracy with respect to the nominal Kalman and static partial-update filters. The superior accuracy and consistency of the proposed nonlinearity-aware partial-update methods are shown via a numerical example.

This article proposes a massively-parallel approach for solving the difficult problem matching high-altitude image pairs from different domains, e.g., Synthetic Aperture Radar (SAR) and visible light imagery (EO). The through-weather measurement capability of SAR allows this technology to yield vehicle position fixes in inclement weather and during either night or daytime for image-aided navigation. This work focuses on developing capabilities to match across a large range of variations in the unknown parameters of the homography that brings these image pairs into correspondence. This is a problem that is not well-solved by any existing approaches and is important in practice as cross-domain imagery from aerial platforms often exhibits large variations in scale, keystone, rotation and translation effects that can be different in the x and y axes. Our approach for cross-modal image matching uses a mutual information loss function and applies a massively-parallel search procedure in CUDA to detect and explore the loss function to find satisfactory homographies to match the image pairs. Experiments are performed using simulated image telemetry obtained by flying a fixed wing aircraft in a virtual environment with image data derived from Google Maps and RADARSAT Google Earth Engine image databases. Results show a comparison rate of 12.79 Gpixel/sec and has a search rate of 1.8M matches/sec allowing for exhaustive search solutions. Our approach is found to yield accurate homography values according to our normalized corner error metric for 68% of the image database pairs.

Synthetic Aperture Radar (SAR) systems sense electromagnetic backscatter from scenes generated from a sequence of excitation pulses of RF radiation emitted from the radar antenna varying spatial positions. Focusing the radar returns into coherent images requires highly accurate knowledge of the antenna position for the duration of the pulses. In this article a massively parallel approach is propose to solve the NP-hard problem of focusing radar data collected in the presence of large motion errors. Little research has been dedicated to the development of focusing algorithms capable of image formation when motion error magnitudes exceed the nominal wavelength of the radar excitation signal. This problem has been shown to be non-deterministic polynomial-time hard (NP-hard) to solve and computational challenges are exacerbated by the high computational cost of associated with the SAR focusing algorithms needed to conduct the search. The proposed approach seeks to address these challenges by restricting trajectories to smooth (low-order) curve trajectories and applying an optimized massively parallel GPU implementation of the SAR focusing algorithm to search over candidate trajectories for the trajectory yielding a focused SAR image.

Synthetic Aperture Radar (SAR) systems emit pulses of (Radio Frequency) RF energy from an antenna into the environment over short intervals in time. Reflected RF energy is received coherently by a receiving antenna and signal processing focusing algorithms process the received signals using one of many potential focusing algorithms to form 2D images of the scene. Formation of these images requires highly accurate knowledge of the antenna state, e.g., the position, orientation and their velocities, when transmitting and receiving RF signals. This information is typically obtained using a combination of a high-quality Global Positioning System (GPS) receiver and a highly-accurate Inertial Navigation System (INS). SAR dependence on GPS and INS data prohibit SAR image formation in GPS-denied contexts which limit the application of this technology. This work investigates approaches for focusing SAR images when GPS is unavailable.

In the absence of an absolute positioning system, such as GPS, autonomous vehicles are subject to accumu-lation of positional error which can interfere with reliable performance. Improved navigational accuracy without GPS enables vehicles to achieve a higher degree of autonomy and reliability, both in terms of decision making and safety. This paper details the use of two navigation systems for autonomous agents using magnetic field anomalies to localize themselves within a map; both techniques use the information content in the environment in distinct ways and are aimed at reducing the localization uncertainty. The first method is based on a nonlinear observability metric of the vehicle model, while the second is an information theory based technique which minimizes the expected entropy of the system. These conditions are used to design guidance laws that minimize the localization uncertainty and are verified both in simulation and hardware experiments are presented for the observability approach.

Robotic airborne chemical source localization has critical applications ranging from search and rescue to hazard detection to pollution assessment. Previous demonstrations on flying robots have required search times in excess of ten minutes, or required computation-intensive signal processing, largely because of the slow response of semiconductor gas sensors. To mitigate these limitations, we developed a hybrid biological/synthetic chemical sensing platform consisting of a moth antenna on an aerial robot. We demonstrate that our robot, a 9 centimeter nano drone, can repeatedly detect and reach the source of a volatile organic chemical plume in less than a minute. We also introduce wind vanes to passively aim the robot upwind, greatly simplifying control. To our knowledge this is the first odor-finding robot to use this approach, and it allows for localization using feedback only from sensors carried on-board rather than GPS, allowing indoor operation. The chemical sensor consists of a hybrid biological/synthetic integrated chemical sensor (electroantennogram) using an excised antenna of the hawkmoth Manduca sexta and associated miniaturized electrophysiology conditioning circuitry. Our robot performs an insect-inspired cast-and-surge search algorithm inspired by the odor-tracking behavior observed in Manduca sexta. These results represent a significant step toward robots that have the speed and sensitivity of biological systems.