A 3D Vision-Based Localization Method for Automated Loading of Large-Sized Heat Exchangers

Manual loading of large-sized finned-tube heat exchangers has low productivity and imposes substantial physical demands on workers. In addition, industrial 3D vision systems often produce large regions of missing depth data when imaging highly reflective metallic surfaces, which complicates grasp-pose estimation for robot manipulators. Unlike randomly sparse measurements, these reflection-induced depth failures are spatially continuous and view-dependent: specular reflection may redirect the projected signal away from the receiver, producing invalid or erroneous depth values over substantial surface regions. Under such conditions, depth-completion methods may generate geometrically plausible but unverified estimates in unobserved regions, whereas correspondence-based registration becomes unreliable when valid overlap, stable local geometry, and distinctive features are simultaneously reduced. This paper develops a model-specific 3D vision-based localization method for automated loading of large-sized heat exchangers affected by structured reflection-induced point-cloud incompleteness. First, an industrial 3D camera synchronously acquires color and depth images. A hybrid interlayer-cardboard extraction strategy uses two-dimensional (2D) color-threshold morphological segmentation and three-dimensional (3D) back-projection as the primary branch, while activating a geometry-based point-cloud fallback when the color-based branch produces no valid candidate or returns a component count inconsistent with the production-task configuration. In 129 paired RGB and point-cloud samples, the color-based branch correctly processed 122 samples, and the fallback recovered the remaining seven cases under the evaluated production-line conditions. For product localization, the method integrates three complementary geometric operations: projected-area minimization for yaw estimation and lateral alignment, tail-feature constraints for head–tail determination and longitudinal anchoring, and inverse mapping of the complete CAD template to the observed product pose. This coordinated procedure exploits the remaining reliable global and regional geometry without reconstructing the missing surface, thereby reducing the pose-estimation bias caused by the shifted centroid of the incomplete point cloud. In the main registration experiment, the method was evaluated using 436 production-line samples with different stacking poses and levels of missing data, including severe cases with a missing rate greater than 30%. This 436-sample evaluation yielded an overall mean translation error of 5.21 mm, a nearest-neighbor RMSE of 1.98 mm, and an average registration-operator runtime of 531.03 ms per registration. By exploiting known product geometry and a reflection-resistant tail feature rather than relying on completed depth values, the proposed method provides deterministic localization for the evaluated heat-exchanger models and production-line conditions.