Visual SLAM-based Localization and Navigation for Service Robots: The Pepper Case
This is an incremental improvement for service robots like Pepper, addressing localization limitations in large spaces.
The authors tackled the problem of enabling the Pepper service robot to self-localize in large environments by extending ORB-SLAM with wheel odometry, resulting in successful navigation tests in a medium laboratory and large hall.
We propose a Visual-SLAM based localization and navigation system for service robots. Our system is built on top of the ORB-SLAM monocular system but extended by the inclusion of wheel odometry in the estimation procedures. As a case study, the proposed system is validated using the Pepper robot, whose short-range LIDARs and RGB-D camera do not allow the robot to self-localize in large environments. The localization system is tested in navigation tasks using Pepper in two different environments: a medium-size laboratory, and a large-size hall.