Accurate visual guidance information is needed when unmanned underwater robots are implementing interactive tasks. Structured light measurement system is widely used in 3D reconstruction of an underwater target because its measuring accuracy is basically unaffected by light refraction and attenuation effects. However

the positioning of an underwater target becomes more difficult considering the underwater unstructured scenarios

the overlap and occlusion between a target and other underwater objects

and the single visual and scattered measurement data. For the problem of position estimation of an underwater man-made object

a 3D object localization algorithm is proposed based on the line feature of an image and the surface feature of a point cloud. Owing to the edge characteristics of the image and surface features of man-made objects

the proposed algorithm describes the object as a combination of line feature and surface feature

and the combination is provided for the localization algorithm as prior information. First

the whole field-of-view image is segmented into several regions of interest (ROIs) that contain different targets

and then the edges of the ROIs are detected. According to the specified line type in prior information

the feature lines are identified from the image edges of the ROIs. The position of the target in the whole field is confirmed with the region of potential target being selected on the basis of identification results. Second

the point cloud that was projected onto the object region is extracted based on the mapping relationship between the image and the point cloud of structured light measurement system. The random sample consensus algorithm is adopted to detect the surface feature from the extracted point cloud

which includes the points of the target

other partial objects

and noise in the field of view. Meanwhile

the parameters of the surface are approximated and the points of the target are extracted. Finally

taking superquadrics as the part-based model for the 3D object

a nonlinear objective function for the 3D object parameter estimation is established using the aforementioned initial approximations. Then

the objective function is minimized using target points

and the optimized parameters can be used as localization result. The reproducibility experiment indicates that the proposed algorithm for ellipse detection is robust; even semi-ellipses can be identified accurately. The validity of the proposed localization algorithm is verified through underwater experiments. For multi-targets whose symmetry axes are parallel to each other

the localization results show that the axis deviations are less than 2°. The relative position deviation of the two spheres

whose center distance is known

is no more than 1%. The time consumed for single-object localization in a complex underwater environment is no longer than 5 s. Unlike other localization algorithms

the proposed algorithm needs to neither segment the point cloud from the target scene nor target offline modeling. Autonomous positioning can be achieved by simply providing the line feature in target imaging edges and the contained surface feature. To verify the validity of the proposed algorithm

some practical experiments are performed. Experimental results show that the proposed algorithm can obtain accurate localization results

is fast and effective for a man-made object of unknown size

and has good applicability to complex underwater environments.