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Abstract-Both landmark measurements and loop closures are used to correct for odometry drift in SLAM solutions. However, if any of the measurements are incorrect (e.g. due to perceptual aliasing) standard SLAM algorithms fail catastrophically and can not return an accurate map. A number of algorithms have been proposed that are robust to loop closure errors, but it is shown in this paper that they can not provide robust solutions when landmark measurement errors occur. The root cause of the problem is that most robust SLAM algorithms only focus on creating a locally consistent map (by evaluating whether measurements appear correct individually) rather than a globally consistent map. This paper proposes a new formulation of the robust SLAM problem that explicitly requires finding a globally consistent solution. Motivated by the new cost function, a novel incremental SLAM algorithm is developed that provides accurate solutions for datasets with landmark or loop closure measurement errors. Simulated and experimental results of the new algorithm, called incremental SLAM with consistency-checking, show that the new algorithm provides significantly more accurate results than state-of-theart robust SLAM methods for datasets with incorrect landmark measurements and can match the performance of current robust SLAM methods for datasets with incorrect loop closures.

This paper presents a new approach to GPS-based navigation which offers significant improvement in antijam capability over traditional designs.The algorithms may be implemented at low cost in software in existing and future GPS receivers using, as inputs, postcorrelation I and Q data and, optionally, raw data from other sensors. Traditional systems are not optimal at high jammer-to-signal (J/S) ratios as a consequence of modular design, use of traditional fixed-gain or gainscheduled tracking loops, and use of artificial moding logic. The approach described here employs a nonlinear filter that operates efficiently at all J/S levels. Filter gains continuously adapt to changes in the J/S environment, and the error covariance propagation is driven directly by measurements to enhance robustness under high jamming and dynamics conditions. Extended-range correlation may be optionally included to increase the code tracking loss-of-lock threshold under high jamming scenarios. Computational complexity is comparable to an extended Kalman filter.Results of hardware-in-the-loop simulations are presented which demonstrate improvements of 15 dB or more in antijam capability relative to traditional designs.

Initial results obtained with a simple, fully automated algorithm for detection of left ventricular boundaries are presented. The strength of this approach is the use of dynamic programming search techniques, which allow determination of local border points to be influenced by the entire global border location. The relative contributions of mask mode subtraction and the dynamic search technique are evaluated with respect to accurate border definition. These computer-determined ventricular borders are compared with hand-traced borders on subtracted and unsubtracted images. The modular dynamic search algorithm is shown to perform better than previously described algorithms, which generally require operator interaction. It is also shown that for both manual and automated techniques, ventricular borders derived from subtracted images may be significantly different from borders derived from nonsubtracted images.

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