For a mobile robot to operate in its environment it is crucial to determine its position with respect to an external reference frame using noisy sensor readings. A scenario in which the robot is moved to another position during its operation without being told, known as the kidnapped robot problem, complicates global localisation. In addition to that, sensor malfunction and external influences of the environment can cause unexpected errors, called outliers, that negatively affect the localisation process. This paper proposes a method based on the fusion of a particle filter with bounded-error localisation, which is able to deal with outliers in the measurement data. The application of our algorithm to solve the kidnapped robot problem using simulated data shows an improvement over conventional probabilistic filtering methods.

Mobile robots can be used in numerous outdoor tasks such as patrolling, delivery and military applications. In order to deploy mobile robots in this kind of environment, where there are different challenges like slopes, elevations, or even holes, they should be able to detect such challenges and determine the best path to accomplish their tasks. In this paper, we are proposing an exploration approach based on potential fields with local distortions, in which we define preferences in uneven terrains to avoid high declivity regions without compromising the best path. The approach was implemented and tested in simulated environments, considering a ground robot embedded with two 2D LIDAR sensors, and the experiments demonstrated the efficiency of our method.

Many applications with mobile robots require self-localization in indoor maps. While such maps can be previously generated by SLAM strategies, there are various localization approaches that use 2D floor plans as reference input. In this paper, we present a localization strategy using floor plan as map, which is based on spatial density information computed from dense depth data of RGB-D cameras. We propose an interval-based model, called Interval Free-Space Density, that bounds the uncertainty of observations and minimizes the effects of movable objects in the environment. Our model was applied in a Monte Carlo Localization strategy and compared with traditional observation models. The results of experiments showed the robustness of the proposed method in single-camera and multi-camera experiments in home environments.

Service robots are becoming a reality across homes. Still, the range of applications supported by such robots remains tied to their ability to self-localize in the environment. Man-made constructions often have a documented blueprint which can be used as input information for robot localization and smart home applications. However, home environments commonly include movable objects and furniture, which can make localization a complicated task, especially for forward-facing horizontal range-finders. In this paper, we present a new and effective global localization approach for home environments which adapts the notion of free space density to a camera pointing to the ceiling. We exploit the available blueprint information, as well as evidence that ceiling vision can provide robust localization information, even in the presence of occlusions. We perform real-world experiments using a robotic vacuum cleaner equipped with an upward-facing camera in two different apartments across multiple trajectories and compare the proposed method with competing approaches. Our solution shows superior localization results using maps where neither furniture or movable objects are not modeled.