Abstract:
Track Loss is a huge problem that occurs during a SLAM process which disturbs the localization and mapping process and it makes the robot dependent
on human supervision. In an ideal scenario, a robot –while performing Visual
SLAM- should be able to move in an unknown environment, prevent obstacles and map each and every corner of the room. But in real cases, even
with perfect obstacle avoidance, there is a chance that the robot may not
map the environment perfectly due to factors such as Camera Blur, Sudden
rotation or feature-less images. In these cases, track is lost since image SIFT
features are not matched frame-by-frame which is a requirement of running
SLAM without interruption. As soon as track is lost, the mapping process is
disrupted, even though the robotic agent doesn’t know, it keeps on moving
which is problematic. Now unless the agent does not come back to a point
from where an image has been made part of the map before, mapping remains halted and the agent is “lost”.
Our Research is to provide the agent with “recovery data” as soon as it becomes lost. As soon as track is lost, the last image of the recorded map and
the existing image extracted from the stopped robot are fed to a pre-trained
classifier. This classifier decides the movement the agent must do in order
to continue mapping. In order to demonstrate our proof of concept, we developed a novel tool by preparing an integrated set up of MINOS Simulator
and ORB SLAM, the agent is able to maneuver over a simulated environment
autonomously while performing SLAM simultaneously. In case of any track
loss, our classifier guides MINOS to recover its tracks.
