  We study a semantic SLAM problem faced by a robot tasked with autonomous
weeding under the corn canopy. The goal is to detect corn stalks and localize
them in a global coordinate frame. This is a challenging setup for existing
algorithms because there is very little space between the camera and the
plants, and the camera motion is primarily restricted to be along the row. To
overcome these challenges, we present a multi-camera system where a side camera
(facing the plants) is used for detection whereas front and back cameras are
used for motion estimation. Next, we show how semantic features in the
environment (corn stalks, ground, and crop planes) can be used to develop a
robust semantic SLAM solution and present results from field trials performed
throughout the growing season across various cornfields.
