  As mobile robot capabilities improve and deployment times increase, tools to
analyze the growing volume of data are becoming necessary. Current
state-of-the-art logging, playback, and exploration systems are insufficient
for practitioners seeking to discover systemic points of failure in robotic
systems. This paper presents a suite of algorithms for similarity-based queries
of robotic perception data and implements a system for storing 2D LiDAR data
from many deployments cheaply and evaluating top-k queries for complete or
partial scans efficiently. We generate compressed representations of laser
scans via a convolutional variational autoencoder and store them in a database,
where a light-weight dense network for distance function approximation is run
at query time. Our query evaluator leverages the local continuity of the
embedding space to generate evaluation orders that, in expectation, dominate
full linear scans of the database. The accuracy, robustness, scalability, and
efficiency of our system is tested on real-world data gathered from dozens of
deployments and synthetic data generated by corrupting real data. We find our
system accurately and efficiently identifies similar scans across a number of
episodes where the robot encountered the same location, or similar indoor
structures or objects.
