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Neves da Fonseca Cardoso Carreira, Fernando Paulo
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- 2D PCA-Based localization for mobile robots in unstructured environmentsPublication . Carreira, Fernando; Christo, C.; Valerio, D.; Ramalho, M.; Cardeira, C.; Calado, João Manuel Ferreira; Oliveira, P.In this paper a new PCA-based positioning sensor and localization system for mobile robots to operate in unstructured environments (e. g. industry, services, domestic ...) is proposed and experimentally validated. The inexpensive positioning system resorts to principal component analysis (PCA) of images acquired by a video camera installed onboard, looking upwards to the ceiling. This solution has the advantage of avoiding the need of selecting and extracting features. The principal components of the acquired images are compared with previously registered images, stored in a reduced onboard image database, and the position measured is fused with odometry data. The optimal estimates of position and slippage are provided by Kalman filters, with global stable error dynamics. The experimental validation reported in this work focuses on the results of a set of experiments carried out in a real environment, where the robot travels along a lawn-mower trajectory. A small position error estimate with bounded co-variance was always observed, for arbitrarily long experiments, and slippage was estimated accurately in real time.
- Navigation system for mobile robots using PCA-based localization from ceiling depth images: experimental validationPublication . Carreira, Fernando; Calado, João Manuel Ferreira; Cardeira, Carlos; Oliveira, P.This paper aims the experimental validation of a mobile robot navigation system, using self-localization based on principal component analysis (PCA) of ceiling depth images. In this approach, a roadmap based on generalized Voronoi diagram (GVD) is built from an occupancy grid, that is defined in the ceiling mapping to the PCA database. The system resorts to the Dijkstra algorithm to planning paths, using the GVD-based roadmap, from which a set of waypoints are extracted. During the mission, the robot is commanded by a controller based on self-located using only the information provided from ceiling depth images and other on-board sensors. The navigation system ensures that the robot reaches its destination, travelling along safety trajectories, while computing its pose with global stable estimates, from Kalman filters (KF). The navigation is achieved without the need to structure the environment, searching by specific features, and to linearize the model. The results are experimentally validated in an indoor environment, using a differential-drive mobile robot.