A computer-implemented method of merging digital datasets in a multidimensional space executed by a processor of an electronic device executing instructions stored on a non-transitory computer-readable medium; the non-transitory computer-readable medium storing the digital datasets comprising: (i) a first dataset having a plurality of first data points, and (ii) a second dataset having a plurality of second data points and a plurality of normal vectors, wherein the first and second data points are LIDAR data points and wherein each normal vector for a given second data point has been calculated based on a neighborhood of LIDAR data points for the given second data point; a given second data point from the plurality of second data points being associated with a respective normal vector from the plurality of normal vectors, the computer implemented method executed by the processor of the electronic device and executing at least an Iterative Closest Point algorithm, the computer implemented method comprising: • matching at least some of the plurality of first data points with at least some of the plurality of second data points, thereby determining a plurality of pairs, a given one of the plurality of pairs including (i) a given first data point and (ii) a given second data point; • determining a pair-specific error value for the given one of the plurality of pairs, the pair-specific error value being indicative of a distance measurement for the given one of the plurality of pairs in the multidimensional space; • determining a weight factor for the given one of the plurality of pairs based on a normal vector associated with the given second data point in the given one of the plurality of pairs, such that the weight factor is inversely proportional to a density of occurrence of the normal vector in a distribution of the plurality of normal vectors; • determining a global error value for the second dataset as a sum of the pair-specific error values having been weighted by the respective weight factors, the global error value being a measurement of a pre-determined error metric that is to be minimized for performing the merging of the digital datasets in the multidimensional space. (i) a first dataset having a plurality of first data points, and (ii) a second dataset having a plurality of second data points and a plurality of normal vectors, wherein the first and second data points are LIDAR data points and wherein each normal vector for a given second data point has been calculated based on a neighborhood of LIDAR data points for the given second data point; a given second data point from the plurality of second data points being associated with a respective normal vector from the plurality of normal vectors, the computer implemented method executed by the processor of the electronic device and executing at least an Iterative Closest Point algorithm, the computer implemented method comprising: • matching at least some of the plurality of first data points with at least some of the plurality of second data points, thereby determining a plurality of pairs, a given one of the plurality of pairs including (i) a given first data point and (ii) a given second data point; • determining a pair-specific error value for the given one of the plurality of pairs, the pair-specific error value being indicative of a distance measurement for the given one of the plurality of pairs in the multidimensional space; • determining a weight factor for the given one of the plurality of pairs based on a normal vector associated with the given second data point in the given one of the plurality of pairs, such that the weight factor is inversely proportional to a density of occurrence of the normal vector in a distribution of the plurality of normal vectors; • determining a global error value for the second dataset as a sum of the pair-specific error values having been weighted by the respective weight factors, the global error value being a measurement of a pre-determined error metric that is to be minimized for performing the merging of the digital datasets in the multidimensional space. • matching at least some of the plurality of first data points with at least some of the plurality of second data points, thereby determining a plurality of pairs, a given one of the plurality of pairs including (i) a given first data point and (ii) a given second data point; thereby determining a plurality of pairs, a given one of the plurality of pairs including (i) a given first data point and (ii) a given second data point; • determining a pair-specific error value for the given one of the plurality of pairs, the pair-specific error value being indicative of a distance measurement for the given one of the plurality of pairs in the multidimensional space; • determining a weight factor for the given one of the plurality of pairs based on a normal vector associated with the given second data point in the given one of the plurality of pairs, such that the weight factor is inversely proportional to a density of occurrence of the normal vector in a distribution of the plurality of normal vectors; • determining a global error value for the second dataset as a sum of the pair-specific error values having been weighted by the respective weight factors, the global error value being a measurement of a pre-determined error metric that is to be minimized for performing the merging of the digital datasets in the multidimensional space.