This paper proposes a point cloud post-processing method based on the minimum spanning tree (MST) and depth-first search (DFS) to extract laser stripe centerlines from the complex inner surfaces of deep holes. Addressing the limitations of traditional image processing methods, which are affected by burrs and low-frequency random noise, this method utilizes 360° structured light to illuminate the inner wall of the deep hole. A sensor captures laser stripe images, and the Steger algorithm is employed to extract sub-pixel point clouds. Subsequently, an MST is used to construct the point cloud connectivity structure, while DFS is applied for path search and noise removal to enhance extraction accuracy. Experimental results demonstrate that this method significantly improves extraction accuracy, with a dice similarity coefficient (DSC) approaching 1 and a maximum Hausdorff distance (HD) of 3.3821 pixels, outperforming previous methods. This study provides an efficient and reliable solution for the precise extraction of complex laser stripes and lays a solid data foundation for subsequent feature parameter calculations and 3D reconstruction.

Deep hole measurement is a crucial step in both deep hole machining and deep hole maintenance. Single-camera vision presents promising prospects in deep hole measurement due to its simple structure and low-cost advantages. However, the measurement error caused by the heating of the imaging sensor makes it difficult to achieve the ideal measurement accuracy. To compensate for measurement errors induced by imaging sensor heating, this study proposes an error compensation method for laser and vision-based deep hole measurement instruments. This method predicts the pixel displacement of the entire field of view using the pixel displacement of fixed targets within the camera’s field of view and compensates for measurement errors through a perspective transformation. Theoretical analysis indicates that the perspective projection matrix changes due to the heating of the imaging sensor, which causes the thermally induced measurement error of the camera. By analyzing the displacement of the fixed target point, it is possible to monitor changes in the perspective projection matrix and thus compensate for camera measurement errors. In compensation experiments, using target displacement effectively predicts pixel drift in the pixel coordinate system. After compensation, the pixel error was suppressed from 1.99 pixels to 0.393 pixels. Repetitive measurement tests of the deep hole measurement instrument validate the practicality and reliability of compensating for thermal-induced errors using perspective transformation.

In this study, a ring light point cloud calibration technique based on collimated laser beams is developed, aiming to reduce errors caused by the position and attitude changes of traditional ring light measurement devices. This article details the generation mechanism of the ring beam and the principle of deep hole measurement. It introduces the collimated beam as a reference, building on traditional ring light measurement devices, to achieve the synchronous acquisition of the ring beam and collimated spot images by an industrial camera. The Steger algorithm is employed to accurately extract the coordinates of the point cloud contours of both the ring beam and the collimated spot. By analyzing the shape and position changes of the collimated spot contour, the spatial position and attitude of the measuring device are precisely determined. This technique is applied to the 3D reconstruction of the inner surface of deep holes, ensuring the accurate restoration of the spatial positional attitude of the ring beam by incorporating the spatial positional attitude parameters of the measuring device to precisely calibrate the cross-sectional point cloud coordinates. Experimental results with ring gauges and deep hole workpieces demonstrate that this technique effectively reduces the percentage of point cloud data outside the tolerance range, and improves the accuracy of the 3D reconstruction model by 6.287%, thereby verifying the accuracy and practicality of this technique.

In this study, an innovative measurement method integrating lateral confocal technology and composite motion control is proposed to address the physical space constraints and data processing problems in the detection of the shape and position errors in deep holes with large aspect ratios and small diameters. By designing a lateral confocal displacement sensor and a cantilever measuring device, we break through the spatial constraints of $\mathrm{\varnothing }6 \mathrm{m}\mathrm{m}$ deep-hole inspection and solve the problems of rigidity and surface damage in the traditional contact probe. We constructed an axis-rotation coordinated motion control model and found that the measuring points were densely arranged in a helical trajectory along the inner wall of the hole. We developed the “virtual slicing–B-spline reconstruction” algorithm and used the adaptive motion control algorithm to achieve a more efficient measurement of the hole. The innovative “virtual slicing–B-spline reconstruction” algorithm, using adaptive grouping, dynamic slicing, and a fourth-order B-spline-fitting hierarchical processing framework, reached a straightness error assessment result of the $1 \mathsf{\mu }\mathrm{m}$ order. Experiments show that, under $0.5 \mathrm{m}\mathrm{m}∕\mathrm{s}$ feed rate and $12 \mathrm{r}\mathrm{m}\mathrm{p}$ rotational speed, the standard deviation of straightness is ≤$0.0008 \mathrm{m}\mathrm{m}$ and the standard deviation of cylindricity is ≤$0.0064 \mathrm{m}\mathrm{m}$; compared to the CMM (coordinate measuring machine) measurement results, the cylindricity and straightness evaluation errors obtained by the new measurement method are reduced by $4.6\%$ and $4.5\%$, respectively. It provides a technical solution that improves both accuracy and efficiency for the precision inspection of small-diameter deep holes.

Addressing the issues of complex calibration processes, limited accuracy, and reliance on high-precision calibration objects in the calibration of circular structured light measurement systems for deep hole precision measurement, this study proposes a light plane calibration method based on monocular vision and geometric constraints to achieve high-precision mapping from two-dimensional pixel coordinates to three-dimensional contour point clouds. The study uses a two-step alignment strategy to complete the coaxial alignment of the camera optical axis and the ring gauge axis by combining calibration plate pose feedback with visual alignment technology based on ellipticity criteria. The optical plane equation is constructed based on the geometric constraint relationship between the two-dimensional pixel coordinates and the inner hole of the ring gauge, and the three-dimensional coordinate calculation is converted into a single-variable depth parameter optimization problem. With the help of the optical plane normal vector, an orthogonal basis is constructed to achieve local coordinate conversion and radius solution of the cross-section point cloud in the camera coordinate system. The results of the ring gauge inner diameter measurement experiment show that the maximum error is 0.005 mm, the standard deviation is 0.003 mm, and the average relative error is 0.008%. This method significantly improves the operability and engineering applicability of the system and provides a reliable technical solution for the efficient and precise inspection of deep hole parts.

To address the issues of uneven accuracy across the entire hole depth and profile distortion caused by multi-source errors in spectral confocal deep-hole measurement, this paper proposes a measurement method involving global calibration using a stepped ring gauge and hierarchical compensation for multi-source errors. By classifying core measurement errors into three categories—geometric deviation, structural error, and dynamic process error—according to their propagation laws, this paper establishes a progressive comprehensive compensation system comprising “geometric calibration–structural correction–dynamic filtering”. Specifically, using a stepped ring gauge as the reference, the system’s intrinsic geometric parameters are identified via the Levenberg–Marquardt (LM) algorithm; structural errors introduced by the deflection of components due to self-weight are quantitatively corrected based on a statics model; periodic harmonic errors are sequentially separated; random noise is effectively suppressed by combining least-squares harmonic fitting with adaptive wavelet threshold filtering. Experimental results demonstrate that this method can limit the maximum absolute deviation in the inner diameter measurement of standard ring gauges to within 0.2 μm, stabilizing the measurement repeatability over the entire depth of deep-hole workpieces with length-to-diameter ratios exceeding 30:1 to within 0.8–1.6 μm, with an expanded uncertainty of $U$ = 3.8 μm ($k$ = 2). This method enables the precise reconstruction of deep-hole inner wall topography, providing a highly versatile technical foundation and implementation scheme for the high-precision non-destructive testing of deep holes with large length-to-diameter ratios.