  Terrestrial laser scanning (TLS) is the standard technique used to create
accurate point clouds for digital forest inventories. However, the measurement
process is demanding, requiring up to two days per hectare for data collection,
significant data storage, as well as resource-heavy post-processing of 3D data.
In this work, we present a real-time mapping and analysis system that enables
online generation of forest inventories using mobile laser scanners that can be
mounted e.g. on mobile robots. Given incrementally created and locally accurate
submaps-data payloads-our approach extracts tree candidates using a custom,
Voronoi-inspired clustering algorithm. Tree candidates are reconstructed using
an adapted Hough algorithm, which enables robust modeling of the tree stem.
Further, we explicitly incorporate the incremental nature of the data
collection by consistently updating the database using a pose graph LiDAR SLAM
system. This enables us to refine our estimates of the tree traits if an area
is revisited later during a mission. We demonstrate competitive accuracy to TLS
or manual measurements using laser scanners that we mounted on backpacks or
mobile robots operating in conifer, broad-leaf and mixed forests. Our results
achieve RMSE of 1.93 cm, a bias of 0.65 cm and a standard deviation of 1.81 cm
(averaged across these sequences)-with no post-processing required after the
mission is complete.
