  We demonstrate the use of semantic object detections as robust features for
Visual Teach and Repeat (VTR). Recent CNN-based object detectors are able to
reliably detect objects of tens or hundreds of categories in a video at frame
rates. We show that such detections are repeatable enough to use as landmarks
for VTR, without any low-level image features. Since object detections are
highly invariant to lighting and surface appearance changes, our VTR can cope
with global lighting changes and local movements of the landmark objects. In
the teaching phase, we build a series of compact scene descriptors: a list of
detected object labels and their image-plane locations. In the repeating phase,
we use Seq-SLAM-like relocalization to identify the most similar learned scene,
then use a motion control algorithm based on the funnel lane theory to navigate
the robot along the previously piloted trajectory. We evaluate the method on a
commodity UAV, examining the robustness of the algorithm to new viewpoints,
lighting conditions, and movements of landmark objects. The results suggest
that semantic object features could be useful due to their invariance to
superficial appearance changes compared to low-level image features.
