  We present a method for detecting and mapping trees in noisy stereo camera
point clouds, using a learned 3-D object detector. Inspired by recent
advancements in 3-D object detection using a pseudo-lidar representation for
stereo data, we train a PointRCNN detector to recognize trees in forest-like
environments. We generate detector training data with a novel automatic
labeling process that clusters a fused global point cloud. This process
annotates large stereo point cloud training data sets with minimal user
supervision, and unlike previous pseudo-lidar detection pipelines, requires no
3-D ground truth from other sensors such as lidar. Our mapping system
additionally uses a Kalman filter to associate detections and consistently
estimate the positions and sizes of trees. We collect a data set for tree
detection consisting of 8680 stereo point clouds, and validate our method on an
outdoors test sequence. Our results demonstrate robust tree recognition in
noisy stereo data at ranges of up to 7 meters, on 720p resolution images from a
Stereolabs ZED 2 camera. Code and data are available at
https://github.com/brian-h-wang/pseudolidar-tree-detection.
