Assessment of handheld mobile terrestrial laser scanning for estimating tree parameters

Sustainable forest management heavily relies on the accurate estimation of tree parameters.Among others,the diameter at breast height(DBH) is important for extracting the volume and mass of an individual tree.For systematically estimating the volume of entire plots,airborne laser scanning(ALS) data are used.The estimation model is frequently calibrated using manual DBH measurements or static terrestrial laser scans(STLS) of sample plots.Although reliable,this method is time-consuming,which greatly hampers its use.Here,a handheld mobile terrestrial laser scanning(HMTLS) was demonstrated to be a useful alternative technique to precisely and efficiently calculate DBH.Different data acquisition techniques were applied at a sample plot,then the resulting parameters were comparatively analysed.The calculated DBH values were comparable to the manual measurements for HMTLS,STLS,and ALS data sets.Given the comparability of the extracted parameters,with a reduced point density of HTMLS compared to STLS data,and the reasonable increase of performance,with a reduction of acquisition time with a factor of5 compared to conventional STLS techniques and a factor of3 compared to manual measurements,HMTLS is considered a useful alternative technique.

Mixed reality head mounted displays for enhanced indoor point cloud segmentation with virtual seeds

Mixed Reality(MR)Head Mounted Displays(HMDs)offer a hitherto underutilized set of advantages compared to conventional 3D scanners.These benefits,inherent to MR-HMDs albeit not originally intended for such appli-cations,encompass the freedom of hand movement,hand tracking capabilities,and real-time mesh visualization.This study leverages these attributes to enhance indoor scanning process.The primary innovation lies in the con-ceptualization of manual-positioned MR virtual seeds for the purpose of indoor point cloud segmentation via a region-growing approach.The proposed methodology is effectively implemented using the HoloLens 2 platform.An application is designed to enable the remote placement of virtual tags based on the user’s visual focus on the MR-HMD display.This non-intrusive interface is further enriched with expedited tag saving and deletion functionalities,as well as augmented tag visualization through overlaying them on real-world objects.To assess the practicality of the proposed method,a comprehensive real-world case study spanning an area of 330 s^(2) is conducted.Remarkably,the survey demonstrates remarkable efficiency,with 20 virtual tags swiftly deployed,each requiring a mere 2 s for precise positioning.Subsequently,these virtual tags are employed as seeds in a region-growing algorithm for point cloud segmentation.The accuracy of virtual tag positioning is found to be exceptional,with an average error of 2.4±1.8 cm.Importantly,the user experience is significantly enhanced,leading to improved seed positioning and,consequently,more accurate final segmentation results.