Robust Iterative Closest Point Algorithm Research Articles

A registration-based stitching method for obtaining high-accuracy material removal distribution in the sub-aperture polishing process

In the research of rapidly developed deterministic sub-aperture polishing technologies, the analysis of material removal distribution is vital for promoting the precision, efficiency, and stability of the polishing process. To obtain a high-accuracy material removal distribution over a large-scale freeform surface with a high lateral resolution, sub-aperture stitching measurements are indispensable. Due to the limitation of traditional stitching methods, it remains a great challenge to obtain precise freeform surface topographies. This paper proposes a registration-based stitching method, which minimizes the negative impact of stitching error, to yield a material removal distribution with both high accuracy and fine lateral resolution. Firstly, to obtain an initial estimate of the rigid transformation for each sub-aperture, conventional stitching and registration based on the least-square stitching method and the robust iterative closest point algorithm are implemented. Secondly, a registration-based stitching model considering the invariance characteristic of the unpolished region is developed to cancel out the inconsistent stitching errors for obtaining a more precise material removal distribution. Finally, a polishing experiment has been conducted to validate the effectiveness of the proposed method. The registration-based stitching method and a commercial stitching program are utilized individually to obtain the material removal distribution. Comparison results show that the proposed method can largely improve the measuring accuracy of material removal distribution, which promotes the further study of the deterministic sub-aperture polishing process.

An efficient LiDAR-based localization method for self-driving cars in dynamic environments

AbstractReal-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method.

Robust iterative closest point algorithm based on global reference point for rotation invariant registration.

The iterative closest point (ICP) algorithm is efficient and accurate for rigid registration but it needs the good initial parameters. It is easily failed when the rotation angle between two point sets is large. To deal with this problem, a new objective function is proposed by introducing a rotation invariant feature based on the Euclidean distance between each point and a global reference point, where the global reference point is a rotation invariant. After that, this optimization problem is solved by a variant of ICP algorithm, which is an iterative method. Firstly, the accurate correspondence is established by using the weighted rotation invariant feature distance and position distance together. Secondly, the rigid transformation is solved by the singular value decomposition method. Thirdly, the weight is adjusted to control the relative contribution of the positions and features. Finally this new algorithm accomplishes the registration by a coarse-to-fine way whatever the initial rotation angle is, which is demonstrated to converge monotonically. The experimental results validate that the proposed algorithm is more accurate and robust compared with the original ICP algorithm.

Robust iterative closest point algorithm with bounded rotation angle for 2D registration

The iterative closest point (ICP) algorithm is efficient to register point sets, but it is easily trapped into a local minimum. The difficulties of obtaining an optimal minimum are the variety of the transformation and finding a suitable initial value. This paper introduces an inequality constraint of the rotation angle into the least square model for 2D point set registration problem and then solves the new model by a more robust ICP approach which bounds the rotation angle of the transformation. In each iteration, a closed-form solution of the transformation is obtained according to the monotonicity of the objective function with respect to the rotation angle. The boundary of rotation angle and initial value are estimated by the principle component analysis. A series of experiments validate that the proposed method is much more robust.

Robust iterative closest point algorithm for registration of point sets with outliers

The problem of registering point sets with outliers including noises and missing data is discussed in this paper. To solve this problem, a novel objective function is proposed by introducing an overlapping percentage for partial registration. Moreover, a novel robust iterative closest point (ICP) algorithm is proposed which can compute rigid transformation, correspondence, and overlapping percentage automatically at each iterative step. This new algorithm uses as many point pairs as possible to yield a more reliable and accurate registration result between two m-D point sets with outliers. Experimental results demonstrate that our algorithm is more robust than the traditional ICP and the state-of-the-art algorithms.