r which changes with time, even in the intake stroke, particularly at the time of transition. To this end, the calculation of the amount of fuel to be injected is performed a plurality of times in the intake stroke of each cylinder so that fuel may be injected on the basis of each calculation. However, this system also involves a problem in that the injected fuel cannot completely enter the combustion chamber during the intake stroke when the engine load is high and the injection pulse width becomes long.

A point cloud is a 3D data set collected by various sensors, such as light detection-and-ranging “LIDAR” sensors, depth cameras, and others. Point cloud registration iteratively aligns a new frame of a 3D data set with previous aligned frames, referred to as a “map.” In many applications, a sensor moves in a 3D space with six degrees of freedom and each new frame relates to a previous frame or to a set of aligned previous frames by a spatial transformation. The registration of a sequence of frames of 3D data set is a process that involves finding the rigid transformations, consisting of translations and rotations, that align the frames in a selected coordinate system.
Point cloud registration has a broad range of applications in areas including computer vision, simultaneous localization and mapping (“SLAM”), robotic move planning, autonomous driving, object recognition, medical imaging, magnetic resonance imaging, virtual and augmented reality, and 3D model construction in remote sensing. Many new applications have become possible, in recent years, due to rapid advances in sensing and computing technologies, as a result of which 3D-data-set registration is becoming an increasingly significant component within many scientific, technological, and commercial applications and fields.
Iterative Closest Point (“ICP”) and Iterative Closest Point (“GICP”) are widely adopted approaches for point-cloud registration. As suggested by its name, ICP depends on iterative searching of 3D spaces and, indeed,