Visual Point‐Line and Inertial Measurement Unit Fusion‐Based Simultaneous Localization and Mapping Algorithm for Robotic Grinding of Wind Turbine Blades

ABSTRACT Various drawbacks exist in traditional visual SLAM methods for wind turbine blade polishing scenarios. Insufficient feature extraction, unstable matching, and reduced positioning accuracy are easily produced. These issues are triggered by weak textures, strong reflections, and illumination fluctuations. A point‐line fused visual‐inertial positioning method named EPL‐VINS is therefore proposed. FAST feature points and EDLines feature lines are adopted in the visual front end of this method. An adaptive threshold feature detection strategy and a texture complexity‐based feature distribution optimization mechanism are put forward, so that the capability of effective feature extraction is strengthened and the spatial distribution of features is optimized. At the back end, a tightly coupled optimization framework based on sliding window is constructed. Marginal prior information, IMU pre‐integration residuals, visual point‐line feature residuals, and loop closure constraints are all integrated into the optimized objective function to realize the joint estimation of visual and inertial information. Meanwhile, the LM optimization algorithm is improved by the dynamic update strategy of damping factors, and the convergence speed and numerical stability of back‐end nonlinear optimization are enhanced. On the public EuRoC data set, an average positioning accuracy improvement of 22.3% is achieved by EPL‐VINS compared with VINS‐Mono. Higher positioning accuracy and environmental adaptability are also exhibited by EPL‐VINS in actual factory scenarios for wind turbine blade polishing. High‐precision robot positioning is realized by the proposed method in complex industrial environments.