Sensor Fusion Algorithms for Improving Autonomous Navigation Accuracy

Abstract

Accurate autonomous navigation requires reliable estimates of position, velocity, and orientation despite noisy measurements, changing environments, and intermittent sensor availability. Sensor fusion combines complementary observations within a common estimation framework to reduce uncertainty and improve continuity. This article presents a narrative review and conceptual framework for navigation-focused sensor fusion, examining nine dimensions: sensor complementarity, calibration and synchronization, Kalman-family estimation, particle filtering, factor-graph optimization, multimodal odometry, robustness, real-time implementation, and performance evaluation. The discussion distinguishes local motion estimation from globally referenced localization and emphasizes that uncertainty reduction depends on valid models, sufficient observability, and correct treatment of correlated information. Representative visual–inertial and lidar–inertial systems illustrate the relationship between sensing and algorithm architecture. An analytical graph shows how correlation limits the uncertainty reduction obtained by averaging two equally precise measurements. It is a mathematical illustration rather than an experimental comparison of navigation algorithms. A proposed implementation workflow combines calibrated sensing, validated probabilistic models, residual monitoring, bounded computational latency, and application-specific benchmarking. The synthesis concludes that adding sensors does not automatically improve accuracy: timing errors, calibration bias, poor data association, and overconfident covariance estimates can undermine fusion. Credible performance claims therefore require consistent reference frames, representative failure scenarios, independent ground truth, and transparent reporting of accuracy, consistency, availability, and execution time.