Contents I. Introduction
Spatial environment sensing and perception (SESP) encompasses the cognitive processes involved in comprehending and navigating the physical space during orientation or wayfinding tasks. Within this framework, the spatial environment denotes the tangible surroundings, and the cognitive agent represents the individual or entity tasked with completing the objective. Consequently, SESP assumes a pivotal role in modern automated and autonomous systems, as these systems heavily depend on perception and sensing techniques. These techniques offer a crucial spatial representation of the environment’s geometry and state, serving as foundational elements for the development of future systems. Strictly speaking, sensing and perception are distinct yet interconnected processes. Sensing, relying on various sensory technologies, generates environmental measurements that serve as inputs for the perception process. In perception, these inputs are selected, organized, and interpreted to construct an understanding of reality. In essence, sensing and perception collaboratively interact to establish a meaningful correspondence with reality [134]. In practical terms, sensing systems encompass tasks such as object detection, motion characteristics, and object attribution, while perception systems handle obstacle semantics localization and tracking, mapping, and environmental semantics, among others [135]. This work embarks on a comprehensive review of sensory technologies, addressing issues related to robustness and reliable operation, along with measurement processing techniques. Notably, there is a specific focus on the increasing prominence of machine learning in recent years.
