Automating Monocular SLAM is challenging as routine trajectory planning frameworks tend to fail primarily due to the inherent tendency of Monocular SLAM systems to break down or deviate largely from their actual trajectory and map states. The reasons for such breakages or large deviations in trajectory estimates are manyfold, ranging from degeneracies associated with planar scenes, with large c...

With respect to the necessity of more autonomous capabilities for mobile robotics in ubiquitous terrains, capable methods on localization and mapping have been developed for the last decade. One of these methods is the Fast SLAM approach which is an extension of the original SLAM problem suggested. In this report we are expressing our methodology for applying a Fast SLAM method based on particl...

The main contribution of this paper is the reformulation of the simultaneous localization and mapping (SLAM) problem for mobile robots such that the mapping and localization can be treated as two concurrent yet separated processes: D-SLAM (decoupled SLAM). It is shown that SLAM can be decoupled into solving a non-linear static estimation problem for mapping and a low-dimensional dynamic estimat...

While many visual simultaneous localisation and mapping (SLAM) systems use point features as landmarks, few take advantage of the edge information in images. Those SLAM systems that do observe edge features do not consider edges with all degrees of freedom. Edges are difficult to use in vision SLAM because of selection, observation, initialisation and data association challenges. However, a map...

In this paper we show that all processes associated to the move-sense-update cycle of EKF SLAM can be carried out in time linear in the number of map features. We describe Divide and Conquer SLAM, an EKF SLAM algorithm where the computational complexity per step is reduced from O(n) to O(n) (the total cost of SLAM is reduced from O(n) to O(n)). In addition, the resulting vehicle and map estimat...

Simultaneous localization and mapping (SLAM) is widely used by autonomous robots operating in unknown environments. Research community has developed numerous SLAM algorithms in the last 10 years. Several works have presented many algorithms’ optimizations. However, they have not explored a system optimization from the system hardware architecture to the algorithmic development level. New comput...

− The simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) problem is not only to solve the SLAM problem in dynamic environments but also to detect and track these dynamic objects. In this paper, we derive the Bayesian formula of the SLAM with DATMO problem, which provides a solid basis for understanding and solving this problem. In addition, we pro...

Extensive research in the field of Visual SLAM for the past fifteen years has yielded workable systems that found their way into various applications, such as robotics and augmented reality. Although filter-based (e.g., Kalman Filter, Particle Filter) Visual SLAM systems were common at some time, non-filter based (i.e., akin to SfM solutions), which are more efficient, are becoming the de facto...

Optimal T cell activation and expansion require engagement of the TCR plus costimulatory signals delivered through accessory molecules. SLAM (signaling lymphocytic activation molecule), a 70-kDa costimulatory molecule belonging to the Ig superfamily, was defined as a human cell surface molecule that mediated CD28-independent proliferation of human T cells and IFN-gamma production by human Th1 a...

Allelic variation of SLAM expression on CD4(+)CD8(+) thymocytes has been proposed to play a major role in NKT cell development. In this article, this hypothesis is tested by the production of subcongenic mouse strains and Slamf1 transgenic lines. The long isoform of the C57BL/6 allele of Slamf1 was transgenically expressed on CD4(+)CD8(+) thymocytes under control of an hCD2 minigene. NOD.Nkrp1b...