  The data loss caused by unreliable network seriously impacts the results of
remote visual SLAM systems. From our experiment, a loss of less than 1 second
of data can cause a visual SLAM algorithm to lose tracking. We present a novel
buffering method, ORBBuf, to reduce the impact of data loss on remote visual
SLAM systems. We model the buffering problem as an optimization problem by
introducing a similarity metric between frames. To solve the buffering problem,
we present an efficient greedy-like algorithm to discard the frames that have
the least impact on the quality of SLAM results. We implement our ORBBuf method
on ROS, a widely used middleware framework. Through an extensive evaluation on
real-world scenarios and tens of gigabytes of datasets, we demonstrate that our
ORBBuf method can be applied to different state-estimation algorithms (DSO and
VINS-Fusion), different sensor data (both monocular images and stereo images),
different scenes (both indoor and outdoor), and different network environments
(both WiFi networks and 4G networks). Our experimental results indicate that
the network losses indeed affect the SLAM results, and our ORBBuf method can
reduce the RMSE up to 50 times comparing with the Drop-Oldest and Random
buffering methods.
