On-the-job detection and correction of systematic cyclic distance measurement errors of terrestrial laser scanners

Abstract Application of terrestrial laser scanning for engineering and documentation projects requiring high accuracy is often prohibitive due to the error budget of terrestrial laser scanners (TLS) which is worse than that of alternative instruments such as total stations. However, by separating the errors of TLS into random and systematic components, the influence of both can be reduced significantly. Approaches for reducing the influence of random noise rely on averaging. One method is introduced briefly as it is relevant for the task of the proposed systematic error removal. We discuss the correction of cyclic distance measurement errors of TLS in detail. For the investigated scanner, these cyclic errors are the most dominant systematic errors. The proposed approach is based on an on-the-job determination of a distance correction function which allows reducing the magnitude of the occurring distance errors by a factor of ten, reducing the systematic errors to less than 0.5 mm. The massive amount of data acquired by TLS is exploited using robust statistical methods. The algorithm developed relies on small, flat surfaces found automatically in the scene. Hence, the method can be applied on engineering project data without requiring additional laboratory experiments. Therefore, it is not essential that the errors are constant over time. By the introduction of on-the-job correction procedures as the one proposed, the systematic errors of TLS can be decreased significantly. This makes them more suitable to be used as an alternative to single point measurement devices.