This paper presents the results of Phase I, Feasibility of Fuel CelVBattery Powered Bus, of a four phase program jointly sponsored by DOE and DOT toward development of a fuel cell powered urban transit bus. The power source design was based on ERC's methanol fueled, air-cooled, atmospheric Phosphoric Acid Fuel Cell (PAFC) and a polymer-bonded nickel-cadmium battery developed by ERC for DOE electric vehicle applications.Fuel celllbattery system trade-off and economic analyses were completed along with fabrication and automatic testing of a half-scale brassboard power source. Testing of the brassboard power source verified the results of the performance analyses for the Test Bed Bus. The technical feasibility and absence of pollutants of the fuel celllbattery hybrid system for vehicle application were demonstrated.

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Purpose -To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scale-variant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to detect scale-and orientation-invariant features as well as provide a robust representation of visual landmarks for SLAM. Design/methodology/approach -SURF are scale-and orientation-invariant features which have higher repeatability than that obtained by other detection methods. Furthermore, SURF algorithms have better processing speed than other scale-invariant detection method. The procedures of detection, description and matching of regular SURF algorithms are modified in this paper in order to provide a robust representation of visual landmarks in SLAM. The sparse representation is also used to describe the environmental map and to reduce the computational complexity in state estimation using extended Kalman filter (EKF). Furthermore, the effective procedures of data association and map management for SURF features in SLAM are also designed to improve the accuracy of robot state estimation. Findings -Experimental works were carried out on an actual system with binocular vision sensors to prove the feasibility and effectiveness of the proposed algorithms. EKF SLAM with the modified SURF algorithms was applied in the experiments including the evaluation of accurate state estimation as well as the implementation of large-area SLAM. The performance of the modified SURF algorithms was compared with those obtained by regular SURF algorithms. The results show that the SURF with less-dimensional descriptors is the most suitable representation of visual landmarks. Meanwhile, the integrated system is successfully validated to fulfill the capabilities of visual SLAM system. Originality/value -The contribution of this paper is the novel approach to overcome the problem of recovering the scale and orientation of visual landmarks in SLAM tasks. This research also extends the usability of local invariant feature detectors in SLAM tasks by utilizing its robust representation of visual landmarks. Furthermore, data association and map management designed for SURF-based mapping in this paper also give another perspective for improving the robustness of SLAM systems. IntroductionTo build a persistent map with robust image features (landmarks) is an important step for implementing the visual simultaneous localization and mapping (SLAM). The image features must have the properties of high repeatability and unique description to be successfully detected at each time step. The research in this paper focuses on the development of algorithms to build...

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