The new generation of laser-based imaging sensors enables collection of range images at video rate at the expense of somewhat low spatial and range resolution. Combining several successive range images, instead of having to analyze each image separately, is a way to improve the performance of feature extraction and target classification. In the robotics community, occupancy grids are commonly used as a framework for combining sensor readings into a representation that indicates passable (free) and non-passable (occupied) parts of the environment. In this paper we demonstrate how 3D occupancy grids can be used for outlier removal, registration quality assessment and measuring the degree of unexplored space around a target, which may improve target detection and classification. Examples using data from amaritime scene, acquired with a 3D FLASH sensor, are shown.