Building and using maps is a fundamental issue for bionic robots in field applications. A dense surface map, which offers rich visual and geometric information, is an ideal representation of the environment for indoor/outdoor localization, navigation, and recognition tasks of these robots. Since most bionic robots can use only small light-weight laser scanners and cameras to acquire semi-dense point cloud and RGB images, we propose a method to generate a consistent and dense surface map from this kind of semi-dense point cloud and RGB images. The method contains two main steps： （1） generate a dense surface for every single scan of point cloud and its corresponding image（s） and （2） incrementally fuse the dense surface of a new scan into the whole map. In step （1） edge-aware resampling is realized by segmenting the scan of a point cloud in advance and resampling each sub-cloud separately. Noine within the scan is reduced and a dense surface is generated. In step （2） the average surface is estimated probabilistically and the non-coincidence of different scans is eliminated. Experiments demonstrate that our method works well in both indoor and outdoor semi-structured environments where there are regularly shaped objects.
Real-time Ethernet(RTE) control systems with critical real-time requirements are called fast real-time(FRT) systems.To improve the real-time performance of Ethernet for plant automation(EPA),we propose an EPA-FRT scheme.The minimum macrocycle of EPA networks is reduced by redefining the EPA network frame format,and the synchronization process is modified to acquire higher accuracy.A multi-segmented topology with a scheduling scheme is introduced to increase effective bandwidth utilization and reduce protocol overheads,and thus to shorten the communication cycle significantly.Performance analysis and practical tests on a prototype system show the effectiveness of the proposed scheme,which achieves the best performance at small periodic payload in large scale systems.
