A method for movable object control, comprising: acquiring a plurality of omnidirectional images of surroundings of a movable object, wherein the omnidirectional images are images having a circular shape, an elliptical shape, or a loop shape; acquiring a plurality of signals having strength changing depending on a location of the movable object; and generating at least one map including the plurality of omnidirectional images of the surroundings of the movable object and the plurality of signals by mapping each omnidirectional image of the surroundings of the movable object and each signal to a point in the surroundings of the movable object; characterised in that the method further comprises: applying the at least one map to a machine learning algorithm to generate a learned algorithm, comprising applying the plurality of omnidirectional images of the surroundings of the movable object included in the map and the plurality of signals to the machine learning algorithm as input values, and applying location information of the corresponding points to the machine learning algorithm as output values, wherein the generation of at least one map on a basis of the signal and the image of the surroundings of the movable object includes: matching one or more signal transceivers (s1-s11) with the strengths of the plurality of acquired signals to acquire a signal vector for each location; comparing patterns of the acquired signal vectors; grouping signals having the same or similar signal vectors and the associated omnidirectional images of the surroundings of the movable object into a plurality of groups; acquiring a plurality of sub-maps for each of the plurality of groups by using a 3D restoration method; and generating the map by combining the plurality of sub-maps. acquiring a plurality of omnidirectional images of surroundings of a movable object, wherein the omnidirectional images are images having a circular shape, an elliptical shape, or a loop shape; acquiring a plurality of signals having strength changing depending on a location of the movable object; and generating at least one map including the plurality of omnidirectional images of the surroundings of the movable object and the plurality of signals by mapping each omnidirectional image of the surroundings of the movable object and each signal to a point in the surroundings of the movable object; characterised in that the method further comprises: applying the at least one map to a machine learning algorithm to generate a learned algorithm, comprising applying the plurality of omnidirectional images of the surroundings of the movable object included in the map and the plurality of signals to the machine learning algorithm as input values, and applying location information of the corresponding points to the machine learning algorithm as output values, wherein the generation of at least one map on a basis of the signal and the image of the surroundings of the movable object includes: matching one or more signal transceivers (s1-s11) with the strengths of the plurality of acquired signals to acquire a signal vector for each location; comparing patterns of the acquired signal vectors; grouping signals having the same or similar signal vectors and the associated omnidirectional images of the surroundings of the movable object into a plurality of groups; acquiring a plurality of sub-maps for each of the plurality of groups by using a 3D restoration method; and generating the map by combining the plurality of sub-maps. matching one or more signal transceivers (s1-s11) with the strengths of the plurality of acquired signals to acquire a signal vector for each location; comparing patterns of the acquired signal vectors; grouping signals having the same or similar signal vectors and the associated omnidirectional images of the surroundings of the movable object into a plurality of groups; acquiring a plurality of sub-maps for each of the plurality of groups by using a 3D restoration method; and generating the map by combining the plurality of sub-maps.