1 research outputs found
ORBBuf: A Robust Buffering Method for Remote Visual SLAM
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 lead to the failure of visual SLAM algorithms. 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, and use an efficient
greedy-like algorithm that drops the frame that results in the least loss to
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
VINSFusion), 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