A generative traversability model for monocular robot self-guidance

The research work disclosed in this publication is partially funded by the Strategic Educational Pathways Scholarship (Malta). The scholarship is part-financed by the European Union - European Social Fund (ESF) under the Operational Programme II - Cohesion Policy 2007-2013, Empowering People for More Jobs and a Better Quality of Life.In order for robots to be integrated into human active spaces and perform useful tasks, they must be capable of discriminating between traversable surfaces and obstacle regions in their surrounding environment. In this work, a principled semi-supervised (EM) framework is presented for the detection of traversable image regions for use on a low-cost monocular mobile robot. We propose a novel generative model for the occurrence of traversability cues, which are a measure of dissimilarity between safe-window and image superpixel features. Our classification results on both indoor and outdoor images sequences demonstrate its generality and adaptability to multiple environments through the online learning of an exponential mixture model. We show that this appearance-based vision framework is robust and can quickly and accurately estimate the probabilistic traversability of an image using no temporal information. Moreover, the reduction in safe-window size as compared to the state-of-the-art enables a self-guided monocular robot to roam in closer proximity of obstacles.peer-reviewe

Performance, robustness and effort cost comparison of machine learning mechanisms in FlatLand

This paper presents the first stage of research into a multi-agent complex environment, called “FlatLand” aiming at emerging complex and adaptive obstacle-avoidance and target achievement behaviors by use of a variety of learning mechanisms. The presentation includes a detailed description of the FlatLand simulated world, the learning mechanisms used as well as an efficient method for comparing the mechanisms’ performance, robustness and required computational effort.peer-reviewe

Archaeology via underwater robots : mapping and localization within Maltese cistern systems

This paper documents the application of several underwater robot mapping and localization techniques used during an archaeological expedition. The goal of this project was to explore and map ancient cisterns located on the islands of Malta and Gozo. The cisterns of interest acted as water storage systems for fortresses, private homes, and churches. They often consisted of several connected chambers, still containing water. A sonar-equipped Remotely Operated Vehicle (ROV) was deployed into these cisterns to obtain both video footage and sonar range measurements. Four different mapping and localization techniques were employed including 1) Sonar image mosaics using stationary sonar scans, and 2) Simultaneous Localization and Mapping (SLAM) while the vehicle was in motion, 3) SLAM using stationary sonar scans, and 4) Localization using previously created maps. Two dimensional maps of 6 different cisterns were successfully constructed. It is estimated that the cisterns were built as far back as 300 B.C.peer-reviewe

Multi-view 3D data acquisition using a single uncoded light pattern

This work is part of the project ’3D-Head’ funded by the Malta Council for Science and Technology under Research Grant No. RTDI-2004-034.This research concerns the acquisition of 3-dimensional data from images for the purpose of modeling a person's head. This paper proposes an approach for acquiring the 3-dimensional reconstruction using a multiple stereo camera vision platform and a combination of passive and active lighting techniques. The proposed one-shot active lighting method projects a single, binary dot pattern, hence ensuring the suitability of the method to reconstruct dynamic scenes. Contrary to the conventional spatial neighborhood coding techniques, this approach matches corresponding spots between image pairs by exploiting solely the redundant data available in the multiple camera images. This produces an initial, sparse reconstruction, which is then used to guide a passive lighting technique to obtain a dense 3-dimensional representation of the object of interest. The results obtained reveal the robustness of the projected pattern and the spot matching algorithm, and a decrease in the number of false matches in the 3-dimensional dense reconstructions, particularly in smooth and textureless regions on the human face.peer-reviewe

Image binarisation using the extended Kalman filter

This work has been mainly supported by Grant 73604 of the University of Malta.Form design is frequently carried out through paper sketches of the designer’s mental model of an object. To improve the time it takes from solution concept to production it would therefore be beneficial if paperbased sketches can be automatically interpreted for importation into three-dimensional geometric computer aided design (CAD) systems. This however requires image pre-processing before initiating the automated interpretation of the drawing. This paper proposes a novel application of the Extended Kalman Filter to guide the binarisation process, thus achieving suitable and automatic classification between image foreground and background.peer-reviewe