Terrain relative navigation can improve the precision of a spacecraft's position estimate by providing supplementary measurements to correct for drift in the inertial navigation system. This paper presents a system, LunaNet, that uses a convolutional neural network to detect craters from camera imagery taken by an onboard camera. These detections are matched with known lunar craters, and these matches can be used as landmarks for localization. The motivation for generating such landmarks is to provide relative location measurements to a navigation filter, however the details of such a navigation filter are not explored within this work. Our results show that on average LunaNet detects approximately twice the number of craters in an intensity image as two other intensity image-based crater detectors. One of the challenges of cameras is that they can generate imagery with vastly different appearances depending on image qualities and noise levels. Differences in image qualities and noise levels can occur for reasons such as changes in irradiance of the lunar surface, heating of camera electronic elements, or the inherent fluctuation of discrete photons. These image noise effects are difficult to compensate for, making it important for a crater detection system to be robust to them. Convolutional neural networks have been demonstrated to be robust to these kinds of imagery variation. LunaNet is shown to be robust to four types of image manipulation that result in changes to image qualities and noise levels of the input imagery.
Data association in SLAM is fundamentally challenging, and handling ambiguity well is crucial to achieve robust operation in real-world environments. When ambiguous measurements arise, conservatism often mandates that the measurement is discarded or a new landmark is initialized rather than risking an incorrect association. To address the inevitable "duplicate" landmarks that arise, we present an efficient map-merging framework to detect duplicate constellations of landmarks, providing a high-confidence loopclosure mechanism well-suited for object-level SLAM. This approach uses an incrementally-computable approximation of landmark uncertainty that only depends on local information in the SLAM graph, avoiding expensive recovery of the full system covariance matrix. This enables a search based on geometric consistency (GC) (rather than full joint compatibility (JC)) that inexpensively reduces the search space to a handful of "best" hypotheses. Furthermore, we reformulate the commonly-used interpretation tree to allow for more efficient integration of clique-based pairwise compatibility, accelerating the branch-and-bound max-cardinality search. Our method is demonstrated to match the performance of full JC methods at significantly-reduced computational cost, facilitating robust object-based loop-closure over large SLAM problems.
Demand for high-performance, robust, and safe autonomous systems has grown substantially in recent years. These objectives motivate the desire for efficient risk estimation that can be embedded in core decision-making tasks such as motion planning. On one hand, Monte-Carlo (MC) and other samplingbased techniques provide accurate solutions for a wide variety of motion models but are cumbersome in the context of continuous optimization. On the other hand, "direct" approximations aim to compute (or upper-bound) the failure probability as a smooth function of the decision variables, and thus are convenient for optimization. However, existing direct approaches fundamentally assume discrete-time dynamics and can perform unpredictably when applied to continuous-time systems ubiquitous in the real world, often manifesting as severe conservatism. State-of-theart attempts to address this within a conventional discretetime framework require additional Gaussianity approximations that ultimately produce inconsistency of their own. In this paper we take a fundamentally different approach, deriving a risk approximation framework directly in continuous time and producing a lightweight estimate that actually converges as the underlying discretization is refined. Our approximation is shown to significantly outperform state-of-the-art techniques in replicating the MC estimate while maintaining the functional and computational benefits of a direct method. This enables robust, risk-aware, continuous motion-planning for a broad class of nonlinear, partially-observable systems.
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