Iteratively mapping-and-approaching an urban area

Abstract

Systems and methods to iteratively map-and-approach an urban area using flying drones. Each of the iterations includes drones flying over the urban area using terrain-following techniques in conjunction with a 3D model that was updated in previous iterations, in which flight paths are defined in conjunction with the 3D model so as to stay away of ground-related features appearing in the 3D model. As the iteration processes progresses, the model becomes increasingly accurate with additional details about the ground related features, thereby allowing the drones to safely approach the ground related features. The iterations converge into a low altitude flight over roads in the urban area, as flying at low altitudes over roads allows the drones to gather information of interest about buildings, infrastructure and assets, in which such information would be difficult to obtain using other techniques. Close proximity to ground related features allows drone delivery functions as well.

What is claimed is: 
     
         1 . A system operative to iteratively map-and-approach an urban area using flying drones, comprising:
 a plurality of drones, each comprising an optical sensor;   a computer comprising a storage space; and   an evolving three-dimensional (3D) model of an urban area, that is stored in the storage space;   wherein the system is configured to:   identify, in the 3D model, using the computer, a set of roads; and   perform a series of map-and-approach iterations toward the roads, in which as part of each of at least some of the iterations, the system is configured to:   generate an expeditionary set of flight paths that are, according to the present evolution level of the 3D model, free of ground-related features situated alongside and/or over the set of roads identified, in which said expeditionary set of flight paths are closer to said ground-related features than flight paths of a previous iteration;   collect, by the computer, data acquired from the optical sensors of drones executing said expeditionary set of flight paths; and   evolve the 3D model to the next level, by the computer, using said data collected, so as to now better include and/or represent the ground-related features situated alongside and/or over the set of roads;   thereby enhancing accuracy of said 3D model while safely approaching closer and closer to said features per each of at least some of the iterations.   
     
     
         2 . The system of  claim 1 , wherein:
 at least some of early ones of the iterations comprise expeditionary set of flight paths that are executed at heights of above twenty meters; and   at least some of later ones of the iterations comprise expeditionary set of flight paths that are executed at heights of below twenty meters.   
     
     
         3 . The system of  claim 2 , wherein said ground-related features situated alongside and/or over the set of roads comprise cables passing above the set of roads and trees located on sidewalks alongside the set of roads;
 in which at least most of said cables and trees are located below said twenty meters above the set of roads.   
     
     
         4 . The system of  claim 3 , wherein at least some of later ones of the iterations comprise expeditionary set of flight paths that are executed above the set of roads and below said cables, while also passing above on-road vehicular traffic. 
     
     
         5 . The system of  claim 1 , wherein said the 3D model is associated with 3D point cloud data structures. 
     
     
         6 . The system of  claim 1 , wherein the drones and/or the computer employ visual-simultaneous-localization-and-mapping (VSLAM) technique using imagery data captured by the optical sensors to generate the evolving 3D model. 
     
     
         7 . The system of  claim 6 , wherein the optical sensors comprise at least one of: (i) cameras and (ii) light-detection-and-ranging (LIDAR) sensor. 
     
     
         8 . The system of  claim 1 , wherein eventually, after many iterations, a detail level of the 3D model allows inclusion of sub-centimeter details associated with cables. 
     
     
         9 . The system of  claim 1 , wherein said iterations are performed during a period of over a month. 
     
     
         10 . The system of  claim 9 , wherein said period allows for exclusion of dynamic objects, such as pedestrians and on-road vehicles, from the 3D model, while including quasi-static objects such as trees, cables and buildings that do not substantially move during said period. 
     
     
         11 . The system of  claim 1 , wherein said computer is located off the drones. 
     
     
         12 . The system of  claim 1 , wherein said computer is located onboard the drones. 
     
     
         13 . The system of  claim 1 , wherein at least some of the drones travel alongside the ground-related features at speeds of above 5 (five) meters-per-second, while avoiding those of the ground-related features that has not been detected in real-time, in which such speeds are possible owing to the 3D model that has been generated iteratively, over time, to include difficult-to-detect features such as cables. 
     
     
         14 . The system of  claim 1 , wherein said expeditionary set of flight paths are defined in the 3D model and relative to 3D representations of the ground-related features that also appear in the 3D model. 
     
     
         15 . The system of  claim 14 , wherein said expeditionary set of flight paths are enclosed by and/or constitute virtual flight corridors that are defined in the 3D model. 
     
     
         16 . The system of  claim 1 , wherein the set of flight paths of a certain iteration are generated in conjunction with the respective present evolution level of the 3D model that was evolved in conjunction with the iteration preceding the certain iteration. 
     
     
         17 . The system of  claim 1 , wherein eventually, after several iterations, the drones are configured to fly within as close as one meter to various of the ground-related features, in which, as a result of said iterations, both a positions of the drones and positions of the features are known to within at least one meter, thereby preventing collisions between the drones and the features. 
     
     
         18 . The system of  claim 17 , wherein the drones are configured to use terrain-following-navigation that comprises correlating real-time imagery with the 3D model, which already exists, thereby determining a current 3D location of the drones relative to the ground-related features that appear in the 3D model. 
     
     
         19 . A method for iteratively map-and-approach an urban area using flying drones, comprising:
 performing, by flying drones, a series of map-and-approach iterations, in which each of at least some of the map-and-approach iterations comprises:   generating an expeditionary set of flight paths that are, according to the present evolution level of a 3D model of an urban area, free of ground-related features that appear in the 3D model and that are associated with a set of roads, and that are closer on average to said set of roads in comparison to flight paths of previous map-and-approach iteration;   flying, by at least some of the drones, along at least parts of the set of flight paths; and   evolving the 3D model using data collected by the drones during said flight, in which said 3D model evolved is used in the next map-and-approach iteration.   
     
     
         20 . The method of  claim 19 , further comprising:
 converging, after several iteration, to an expeditionary set of flight paths that passes right above the length of at least some of the set of roads, and at an average height of just between 2 (two) to 5 (five) meters above the set of roads.