Advancements in robotic positioning: A comprehensive review of measurement methods and systems

Robotic positioning plays a crucial role in enabling accurate navigation, control, and autonomy across diverse domains, including industrial automation, autonomous vehicles, unmanned aerial vehicles (UAVs), underwater exploration, swarm robotics, and biomedical systems. Despite notable advancements, challenges such as sensor drift, environmental variability, computational complexity, and energy constraints continue to affect positioning accuracy. To address these limitations, diverse measurement systems and sensor fusion techniques have been developed, integrating inertial measurement units (IMUs), Global Positioning System (GPS), Light Detection and Ranging (LIDAR), optical motion capture, ultrasonic sensors, radio-frequency identification (RFID), and infrared (IR) tracking. This review introduces a structured classification of positioning systems into on-board, out-board, and hybrid types, highlighting trade-offs and application-specific strengths. Measurement methods are categorized into relative and absolute approaches. Furthermore, artificial intelligence (AI)-driven fusion strategies such as Kalman filtering, particle filtering, visual–inertial odometry (VIO), and simultaneous localization and mapping (SLAM) are analyzed for their roles in enhancing robustness and mitigating drift. Evaluation is based on accuracy, latency, efficiency and adaptability. Cross-domain comparisons illustrate how sensor-algorithm integration impacts outcomes across aerial, mobile, underwater, industrial and biomedical platforms. Finally, emerging directions such as 6G-enabled ultra-precise localization, neuromorphic computing for low-power SLAM, and blockchain-based decentralized frameworks are proposed to improve trust, adaptability, and reliability in next-generation robotic autonomy. • Comprehensive review of robotic positioning sensors and measurement systems. • Evaluates AI-enhanced sensor fusion techniques for adaptive robot navigation. • Analyzes uncertainty modeling and error mitigation in real-time positioning. • Explores neuromorphic, blockchain, and quantum trends in metrological robotics. • Proposes future pathways for traceable and autonomous robot localization systems.