Simultaneous localization and mapping (SLAM) is a fundamental function of intelligent robots. To reduce the influence dynamic objects on SLAM in environments, this study pro-poses visual based sequential image segmentation, referred to as SIIS-SLAM. Based ORB-SLAM3, SIIS-SLAM integrates instance segmentation optical flow detection module. The module designed eliminate effectiveness estimation r...

The computational complexity of SLAM is dominated by the cost of factorizing a matrix derived from the measurements into a square root form, which has cubic complexity in the worst case. However, the matrices associated with the full SLAM problem are typically very sparse, as opposed to the dense problems one obtains in a filtering context. Hence much faster, sparse factorization algorithms can...

Navigating an unexplored environment using simultaneous localization and mapping (SLAM) requires that the robot’s trajectory include revisit actions in order to produce loop-closure constraints; however, efficient area coverage requires that the robot’s trajectory be minimally redundant in its path. This paper reports on a next-best-view SLAM algorithm that balances the trade-off between explor...

Quite a number of approaches for solving the simultaneous localization and mapping (SLAM) problem exist by now. Some of them have recently been extended to mapping environments with six-degree-of-freedom poses, yielding 6D SLAM approaches. To demonstrate the capabilities of the respective algorithms, it is common practice to present generated maps and successful loop closings in large outdoor e...

This paper reports on an integrated navigation algorithm for the visual simultaneous localization and mapping (SLAM) robotic area coverage problem. In the robotic area coverage problem, the goal is to explore and map a given target area within a reasonable amount of time. This goal necessitates the use of minimally redundant overlap trajectories for coverage efficiency; however, visual SLAM’s n...

The contact-SLAM problem is a broad class of grasping and manipulation problem and it is very important to robotic manipulation tasks, particularly when contacts are likely to be intermittent. Several researchers have developed particle filters for C-SLAM problems that estimate the state of manipulated objects, some geometric properties, and their contacts, but the effects of various designing ...

Existing simultaneous localization and mapping (SLAM) algorithm is not robust in challenging low-texture environments because of few salient features. The resulting sparse or semi-dense map also conveys little information for motion planning. Though some work utilize plane or scene layout for dense map regularization, they require decent state estimation from other sources. In this paper, we pr...

This paper describes a Bayesian formulation of the Simultaneous Localisation and Mapping (SLAM) problem. Previously, the SLAM problem could only be solved in real time through the use of the Kalman Filter. This generally restricts the application of SLAM methods to domains with straight-forward (analytic) environment and sensor models. In this paper the Sum-of-Gaussian (SOG) method is used to a...

Modern dense visual SLAM systems produce high-fidelity geometric models, yet the quality of their textures lags behind. In part, the problem pertains to naive handling of colors. The RGB triplets from images are averaged straight into the model, ignoring nonlinearity of the image color space, vignetting effects, and variations of exposure time between different frames. In this paper we propose ...

Autonomous mobile robots operating in a priori unknown environments must be able to integrate path planning with simultaneous localization and mapping (SLAM) in order to perform tasks like exploration, search and rescue, inspection, reconnaissance, target-tracking, and others. This level of autonomy is especially difficult in underwater environments, where GPS is unavailable, communication is l...