Landmark EgoSphere

The Landmark EgoSphere (LES) contains information about the angular distribution of the landmarks that the robot expects to see at a goal position, and is similar to the Sensory EgoSphere (SES) in structure. For navigational tasks we often use a simplified EgoSphere structure, in which the original 3-D shape of the EgoSphere is projected down to a 2-D structure. In our current research, the SES is used to represent the robot’s perception of its environment at its current state and the LES is used to represent the robot’s expected perception of its environment at a target position that the robot is trying to reach. The SES is a result of robot’s perception. The LES may be obtained in several ways. For example, it may be the result of the robot’s perception on a previous navigational mission, the result of another robot’s perception at the target point, or it may be derived from a rough map of the area. (Figure 1).

Figure 1. Landmark EgoSphere (LES) diagram

In association with the Egocentric Navigation algorithm (ENav) we have developed, we have two methods that demonstrate the communication of perceptual knowledge, represented by the SES and the LES, among humans and robots. The first method is a knowledge sharing method in a team of two heterogeneous robots with the emphasis given on sharing LES information between the robots. The second method is a visual perception correction method where the robot shares its understanding of the environment, via its current SES, with the human so that the human may correct possible misperceptions by the robot.

The LES is a robo-centric representation of environmental features expected at a target position. In other words, the LES contains information about the angular distribution of the landmarks that the robot expects to see at a goal position, and is similar to the SES in structure (Figure 2a). The LES can be extracted from a map of the area, or it can be derived from some other description of the region. Furthermore, a previously acquired SES stored in the memory of any robot in a team of robots can be used as an LES (Figure 2b).

Figure 2a. Overview map of outdoor locale for LES identifications for LES in 2b


Figure 2b. LES representation to be used by robot teaming