Mobile robot navigation in unstructured environment is a challenging task due to the uncertain nature of the real world. Navigating using visual landmarks could be a mandatory skill together with the ability of building a representation of the world around the robot. This mapping aptitude should be implemented as an efficient real-time task, even if a large number of elements have to be include...

In this work, we examine the problem of constructing and maintaining a map of an autonomous vehicle’s environment for the purpose of navigation, using evidential reasoning. The inherent uncertainty in the origin of measurements of sensors demands a probabilistic approach to processing, or fusing, the new sensory information to build an accurate map. In this paper, the map is based on a two-dime...

This paper proposes a topological mapping technique that utilises persistent SIFT features to reduce the amount of data storage required. It delivers, as an output, a topological map that lends itself well to conventional path planning techniques. The approach assimilates features into a statistical model which promises improved data association. Experiments were performed using omnidirectional...

Article history: Accepted 11 March 2016 Available online 9 April 2016 Three dimensional (3D)mapping of environments has gained tremendous attention, from academic to industry to military, owing to the ever increasing needs of environmental modeling as well as monitoring. While many highly effective techniques have been reported thus far, a few even turned into commercial products, none has expl...

This paper presents the theoretical aspects and a preliminary experimental validation of a criterion for determining the best observation positions for robot map building, intended as a particular environmental measurement task. The proposed criterion takes into account both the distance travelled and the information gathered by the robot.

We describe a method for the reconstruction of a driven trajectory of a mobile robot if the begin and end states of the trajectory are known, and intermediate readings from odometry are available. Our method uses a Kalman filter to combine a forward and backward dead-reckoning trajectory. We show that our method is more reliable for long trajectories than just combining the dead-reckoning traje...

Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. We believe that the ability to learn such semantic categories from sensor data or in maps enables a mobile robot to more efficiently accomplish a variety of tasks such as human-robot interaction, path-planning, exploration, or localization. In this work, w...

Simultaneous Localization and Mapping (SLAM) has been one of the active research areas in robotic community for the past decade of years. SLAM addresses the problem of a robot navigating and building a map of an unknown environment, without an initial map or an absolute localization means. This paper attempts to provide a comprehensive overview of the SLAM problem. Successful SLAM implementatio...

The capacity to know about the environment is a major requirement for robots and automated systems. Real scenes are complex to perceive, dynamic and large. Therefore to perceive those scenes, robots have access to complementary sensors such as sonar/laser range-finders, cameras or bumpers, but all these are always noisy. In automated navigation, the internal representation of the environment is...

This paper presents a map-based system in which local residents can supply and share disaster prevention information. Producing a disaster prevention map with public participation is very useful as learning and awareness building for natural disasters. Our system's unique feature introduces a card-image data structure, which makes it easy for users to add new information, such as information ab...