  Sequential matching using hand-crafted heuristics has been standard practice
in route-based place recognition for enhancing pairwise similarity results for
nearly a decade. However, precision-recall performance of these algorithms
dramatically degrades when searching on short temporal window (TW) lengths,
while demanding high compute and storage costs on large robotic datasets for
autonomous navigation research. Here, influenced by biological systems that
robustly navigate spacetime scales even without vision, we develop a joint
visual and positional representation learning technique, via a sequential
process, and design a learning-based CNN+LSTM architecture, trainable via
backpropagation through time, for viewpoint- and appearance-invariant place
recognition. Our approach, Sequential Place Learning (SPL), is based on a CNN
function that visually encodes an environment from a single traversal, thus
reducing storage capacity, while an LSTM temporally fuses each visual embedding
with corresponding positional data -- obtained from any source of motion
estimation -- for direct sequential inference. Contrary to classical two-stage
pipelines, e.g., match-then-temporally-filter, our network directly eliminates
false-positive rates while jointly learning sequence matching from a single
monocular image sequence, even using short TWs. Hence, we demonstrate that our
model outperforms 15 classical methods while setting new state-of-the-art
performance standards on 4 challenging benchmark datasets, where one of them
can be considered solved with recall rates of 100% at 100% precision, correctly
matching all places under extreme sunlight-darkness changes. In addition, we
show that SPL can be up to 70x faster to deploy than classical methods on a 729
km route comprising 35,768 consecutive frames. Extensive experiments
demonstrate the... Baseline code available at
https://github.com/mchancan/deepseqslam
