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It is well known that insects and other animals use olfactory senses in a wide variety of behavioural processes, namely to recognize and locate food sources, detect predators, and find mates. This article discusses the gathering of olfactive information and its utilization by a mobile robot to find a specific odour source in a room with turbulent phenomena's and multiple sources of odour. Three navigation algorithms are compared with a simple gas sensor and with an electronic nose. Their performance in finding an ethanol source in a room with obstacles is evaluated. The first navigation strategy is based on bacteria chemotaxis. The second strategy is based on the male silkworm moth algorithm that is used to search and track a female moth pheromone plume. The last strategy is based on the estimation of odour geometry and gradient tracking. The electronic nose utilized is composed by an array of different and weakly selective metal oxide gas sensors. The odours are identified and quantified by a pattern recognition algorithm based on an artificial neural network. The test bed for the navigation algorithms was a Nomad Super Scout II mobile robot. ᮊ

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This paper presents a new algorithm for the extrinsic calibration of a perspective camera and an invisible 2D laser-rangefinder (LRF). The calibration is achieved by freely moving a checkerboard pattern in order to obtain plane poses in camera coordinates and depth readings in the LRF reference frame. The problem of estimating the rigid displacement between the two sensors is formulated as one of registering a set of planes and lines in the 3D space. It is proven for the first time that the alignment of three plane-line correspondences has at most eight solutions that can be determined by solving a standard p3p problem and a linear system of equations. This leads to a minimal closed-form solution for the extrinsic calibration that can be used as hypothesis generator in a RANSAC paradigm. Our calibration approach is validated through simulation and real experiments that show the superiority with respect to the current state-of-the-art method requiring a minimum of five input planes.

A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a single camera is presented. Two sensor fusion architectures are described, a centralized and a decentralized one. In the former, the fusion process occurs at the feature level, i.e., features from LIDAR and vision spaces are combined in a single vector for posterior classification using a single classifier. In the latter, two classifiers are employed, one per sensor-feature space, which were offline selected based on information theory and fused by a trainable fusion method applied over the likelihoods provided by the component classifiers. The proposed schemes for sensor combination, and more specifically the trainable fusion method, lead to enhanced detection performance and, in addition, maintenance of false-alarms under tolerable values in comparison with singlebased classifiers. Experimental results highlight the performance and effectiveness of the proposed pedestrian detection system and the related sensor data combination strategies.C 2009 Wiley Periodicals, Inc.

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Abstract-A feature detection system has been developed for real-time identification of lines, circles and legs from laser data. A new method suitable for arc/circle detection is proposed: the Internal Angle Variance (IAV). Lines are detected using a recursive line fitting method. The people leg detection is based on geometrical constrains. The system was implemented as a fiducial driver in Player, a mobile robot server. Real results are presented to verify the effectiveness of the proposed algorithms in indoor environment with moving objects.

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