Trail-Map-based homing under the presence of sensor noise

The travel range of a mobile robot is directly linked to its energy consumption. Apart from the actuators, the computing resources are the main energy consumers. Thus, robots that can navigate with minimal computational resources would be able to travel longer distances and, hence, would be valuable tools in applications such as search and rescue or planetary exploration. Inspired from the navigational abilities of insects, we developed the Trail-Map as a data structure for biologically inspired homing that can easily be scaled in case of memory or computational shortage. The Trail-Map can be built in constant time and enables constant time homing vector calculation. In this paper, we evaluate the Trail-Map-based homing performance of a simulated mobile robot equipped with an omnidirectional camera under the presence of sensor noise, such as odometry errors and observation errors. Further, we will show that Trail-Map-based homing outperforms SLAM methods in terms of computational resources while achieving a comparable homing performance.