Sensor Fusion for Mobile Robot Navigation

TLDR

Sensor fusion is essential for mobile robots that use multiple complementary or redundant sensors to build maps, localize themselves, and detect objects. This review surveys sensor‑fusion techniques for robot navigation, focusing on algorithms that enable accurate self‑location. The authors categorize fusion methods into low‑level integration for direct data fusion and high‑level integration for hierarchical control, and discuss tools such as Kalman filtering, rule‑based systems, behavior‑based algorithms, information‑theoretic approaches, Dempster‑Shafer reasoning, fuzzy logic, and neural networks.

Abstract

We review techniques for sensor fusion in robot navigation, emphasizing algorithms for self-location. These find use when the sensor suite of a mobile robot comprises several different sensors, some complementary and some redundant. Integrating the sensor readings, the robot seeks to accomplish tasks such as constructing a map of its environment, locating itself in that map, and recognizing objects that should be avoided or sought. The review describes integration techniques in two categories: low-level fusion is used for direct integration of sensory data, resulting in parameter and state estimates; high-level fusion is used for indirect integration of sensory data in hierarchical architectures, through command arbitration and integration of control signals suggested by different modules. The review provides an arsenal of tools for addressing this (rather ill-posed) problem in machine intelligence, including Kalman filtering, rule-based techniques, behavior based algorithms, and approaches that borrow from information theory, Dempster-Shafer reasoning, fuzzy logic and neural networks.