Visual homing is a lightweight approach to visual navigation to direct a single robot to a visual landmark without the use of a map or any form of GNSS. However, applications that require a team of robots to operate robustly with respect to map and GNSS requirements, may also require groups of robots to move together and/or in a coordinated fashion. Examples include agricultural robotics where several robots must coordinate to deliver material to a visually identified rallying point, or a reconnaissance and surveillance mission where robots must move towards a (potentially moving) visual target in a formation. We present and evaluate several visual homing algorithms extended to handle team homing. We present evaluation results for stationary rallying points and moving target examples using a ROS/Gazebo simulated urban environment and real Turtlebot3 robots. We show that our algorithms produce accuracies within a 95% confidence interval in both simulation and physical experiments.
Visual homing is a lightweight approach to visual navigation which does not require GPS. It is very attractive for robot platforms with a low computational capacity. However, a limitation is that the stored home location must be initially within the field of view of the robot. Motivated by the increasing ubiquity of camera information we propose to address this line-of-sight limitation by leveraging camera information from other robots and fixed cameras. To home to a location that is not initially within view, a robot must be able to identify a common visual landmark with another robot that can be used as an ‘intermediate’ home location. We call this intermediate location identification step the “Do you see what I see” (DYSWIS) task. We evaluate three approaches to this problem: SIFT based, CNN appearance based, and a semantic approach.
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