This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural
scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine
learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping
for the efficient identification of objects composing unknown environments; classification-based rapid object recognition
for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working
memory mechanism and its biological processes for human-like localization; and the application of this present understanding
to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations
and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.