The objective of this work is to develop a data processing system that can automatically generate waypoints for navigation of an unmanned aerial vehicle (UAV) to inspect surfaces of structures like buildings and bridges. The input includes data recorded by two 2D laser scanners, orthogonally mounted on the UAV, and an inertial measurement unit (IMU). To achieve the goal, algorithms are developed to process the data collected. They are separated into three major groups: (i) the data registration and filtering to generate a 3D model of the structure and control the density of point clouds for data completeness enhancement; (ii) the surface and obstacle detection to assist the UAV in monitoring tasks; and (iii) the waypoint generation to set the flight path. Experiments on different data sets show that the developed system is able to reconstruct a 3D point cloud of the structure, extract its surfaces and objects, and generate waypoints for the UAV to accomplish inspection tasks.

Motivation: The approaches usually used for building large genetic maps consist of dividing the marker set into linkage groups and provide local orders that can be tested by multi-point linkage analysis. To deal with the limitations of these approaches, a strategy taking the marker set into account globally is defined. Results: The paper presents a new approach called 'Bi-Dimensional Scaling Map (BDS-Map) for inferring marker orders and distances in genetic maps based on the use of an additional dimension orthogonal to the map into which markers are projected. Dynamical forces based on a two-point analysis are applied to tend to optimize the marker locations in space. The efficiency of the approach is exemplified on real data (16 and 70 markers on chromosomes 6 and 2, respectively) and simulated data (50 maps of 70 markers). Availability: The package called ANGE (ANalyse du GE-nome), including both the program BDS-Map and the system allowing the database management, is available on request from the authors. Contact: E-mail:[email protected]