  Simultaneous localization and mapping (SLAM) remains challenging for a number
of downstream applications, such as visual robot navigation, because of rapid
turns, featureless walls, and poor camera quality. We introduce the
Differentiable SLAM Network (SLAM-net) along with a navigation architecture to
enable planar robot navigation in previously unseen indoor environments.
SLAM-net encodes a particle filter based SLAM algorithm in a differentiable
computation graph, and learns task-oriented neural network components by
backpropagating through the SLAM algorithm. Because it can optimize all model
components jointly for the end-objective, SLAM-net learns to be robust in
challenging conditions. We run experiments in the Habitat platform with
different real-world RGB and RGB-D datasets. SLAM-net significantly outperforms
the widely adapted ORB-SLAM in noisy conditions. Our navigation architecture
with SLAM-net improves the state-of-the-art for the Habitat Challenge 2020
PointNav task by a large margin (37% to 64% success). Project website:
http://sites.google.com/view/slamnet
