Abstract
Abstract. A system was developed for automatic estimations of tree positions and stem diameters. The sensor trajectory was first estimated using a positioning system that consists of a low precision inertial measurement unit supported by image matching with data from a stereo-camera. The initial estimation of the sensor trajectory was then calibrated by adjustments of the sensor pose using the laser scanner data. Special features suitable for forest environments were used to solve the correspondence and matching problems. Tree stem diameters were estimated for stem sections using laser data from individual scanner rotations and were then used for calibration of the sensor pose. A segmentation algorithm was used to associate stem sections to individual tree stems. The stem diameter estimates of all stem sections associated to the same tree stem were then combined for estimation of stem diameter at breast height (DBH). The system was validated on four 20 m radius circular plots and manual measured trees were automatically linked to trees detected in laser data. The DBH could be estimated with a RMSE of 19 mm (6 %) and a bias of 8 mm (3 %). The calibrated sensor trajectory and the combined use of circle fits from individual scanner rotations made it possible to obtain reliable DBH estimates also with a low precision positioning system.
Highlights
Forest inventories are still conducted using manual measurements on sample plots
The position estimates for Mobile Laser Scanning (MLS) systems are usually obtained through a combined use of an inertial measurement unit (IMU) and a Global Navigation Satellite System (GNSS)
The system presented in this study consists of (1) a positioning system for an initial estimate of the sensor trajectory, (2) a laser scanner operated with multiple lasers each separated with different angles relatively the horizontal plane of the scanner, (3) algorithms
Summary
Forest inventories are still conducted using manual measurements on sample plots. there is a rapid development of sensors which can produce point clouds of 3D measurements and automate the process of forest inventory. The position estimates for Mobile Laser Scanning (MLS) systems are usually obtained through a combined use of an inertial measurement unit (IMU) and a Global Navigation Satellite System (GNSS). One solution is to use Simultaneous Localization And Mapping (SLAM) algorithms which use laser scanner data from surrounding objects to estimate the sensor position. These algorithms are usually efficient for indoor applications with welldefined planar surfaces in many directions but the problem becomes more difficult in outdoor environments. Some algorithms have recently been developed that efficiently use the geometry of the traversed surroundings, both indoor and in outdoor environments, without the need of high precision inertial measurements (Bosse et al, 2012, Zhang and Singh, 2014). The system presented in this study consists of (1) a positioning system for an initial estimate of the sensor trajectory, (2) a laser scanner operated with multiple lasers each separated with different angles relatively the horizontal plane of the scanner, (3) algorithms
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