On Foveated Gaze Control and Combined Gaze and Locomotion Planning

This chapter presents recent research results of our laboratory in the area of vision and locomotion coordination with an emphasis on foveated multi-camera vision. A novel active vision planning concept is presented which coordinates the individual devices of a foveated multi-camera system. Gaze direction control is combined with trajectory planning based on information theoretic criteria to provide vision-based autonomous exploring robots with accurate models of their environment. With the help of velocity and yaw angle sensors, mobile robots can update the internal knowledge about their current position and orientation from a previous time step; this process is commonly referred to as dead-reckoning. Due to measurement errors and slippage these estimations are erroneous and position accuracy degrades over time causing a drift of the estimated robot pose. To overcome the drift problem it is common to take absolute measurements evaluating visual information, which are fused dynamically with the odometry data by applying Kalman-filter or other techniques, e.g. (Dissanayake et al., 2001). The use of active vision systems for navigation is state-of-the-art providing a situation-related selective allocation of vision sensor resources, e.g. (Davison & Murray, 2002; Seara et al., 2003; Vidal-Calleja et al., 2006). Active vision systems comprising only one type of vision sensor face a trade-off between field of view and measurement accuracy due to limitations of sensor size and resolution, and of computational resources. In order to overcome this drawback the combined use of several vision devices with different fields of view and measurement accuracies is known which is called foveated, multi-resolution, or multi-focal vision, e.g. cf. (Dickmanns, 2003; Kuhnlenz et al., 2006; Ude et al., 2006). Thereby, the individual vision devices can be independently controlled according to the current situation and task requirements. The use of foveated active vision for humanoid robot navigation is considered novel. Active vision is also frequently utilized in the context of robotic exploration. Yet, gaze control and locomotion planning are generally decoupled in state-of-the-art approaches to simultaneous localization and mapping (SLAM). An integrated locomotion planning and gaze direction control concept maximizing the collected amount of information is presented in the second part of this chapter. This strategy results in more accurate autonomously acquired environment representations and robot position estimates compared to state-of-the-art approaches. The chapter is organized as follows: In Section 2 vision-based localization and mapping in the context of humanoid robots is surveyed; Section 3 is concerned with foveated multi-