Range imagers provide useful information for part inspection, robot control, or human safety applications in industrial environments. However, some applications may require more information than range data from a single viewpoint. Therefore, multiple range images must be combined to create a three-dimensional representation of the
scene. Although simple in its principle, this operation is not straightforward to implement in industrial systems, since each range image is affected by noise. In this paper, we present two specific applications where merging of range images must be performed. We use the same processing pipeline for both applications : conversion from
range image to point clouds, elimination of degrees of freedom between different clouds, validation of the merged results. Nevertheless, each step in this pipeline requires dedicated algorithms for our example applications. The first application is high resolution inspection of large parts, where many range images are acquired sequentially and merged in a post-processing step, allowing to create a virtual model of the part observed, typically larger than the instrument's field of view. The key requirement in this application is high accuracy for the merging of multiple point clouds. The second application discussed is human safety in a human/robot environment: range images are used to ensure that no human is present in the robot’s zone of operation, and can trigger the robot's emergency shutdown when needed. In this case, range image merging is required to avoid uncertainties due to occlusions. The key requirement here is real-time operation, namely the merging operation should not introduce a significant latency in the data processing pipeline. For both application cases, the improvements brought by
merging multiple range images are clearly illustrated.