  This paper describes an end-to-end pipeline for tree diameter estimation
based on semantic segmentation and lidar odometry and mapping. Accurate mapping
of this type of environment is challenging since the ground and the trees are
surrounded by leaves, thorns and vines, and the sensor typically experiences
extreme motion. We propose a semantic feature based pose optimization that
simultaneously refines the tree models while estimating the robot pose. The
pipeline utilizes a custom virtual reality tool for labeling 3D scans that is
used to train a semantic segmentation network. The masked point cloud is used
to compute a trellis graph that identifies individual instances and extracts
relevant features that are used by the SLAM module. We show that traditional
lidar and image based methods fail in the forest environment on both Unmanned
Aerial Vehicle (UAV) and hand-carry systems, while our method is more robust,
scalable, and automatically generates tree diameter estimations.
