Abstract: A system and method for object detection in 3-dimensional space uses a ground up model that makes detecting objects faster and more accurate by combining the error correction and object detection, while also using flat planes as a basis for the mathematics. Two or more 3D scanning devices, such as scanning LIDARs, collect a 3D dataset for a field of view (FOV). The FOV is divided into boxes with a small degree of overlap, then each box is processed iteratively to identify features of an object contained within the box. Box size is chosen so that the portion of the object contained within the box can be assumed to be planar. Adjoining boxes are evaluated for shared data points then merged it the form part of the same object.

Abstract: A system and method for object detection in 3-dimensional space uses a ground up model that makes detecting objects faster and more accurate by combining the error correction and object detection, while also using flat planes as a basis for the mathematics. Two or more 3D scanning devices, such as scanning LIDARs, collect a 3D dataset for a field of view (FOV). The FOV is divided into boxes with a small degree of overlap, then each box is processed iteratively to identify features of an object contained within the box. Box size is chosen so that the portion of the object contained within the box can be assumed to be planar. Adjoining boxes are evaluated for shared data points then merged it the form part of the same object.