Spherical Laser Point Sampling with Application to 3D Scene Genetic Registration Jorge L. Mart´ ınez, Antonio Reina and Anthony Mandow Dep. Ingenier´ ıa de Sistemas y Autom´ atica, E.T.S. Ingenieros Industriales Universidad de M´ alaga, Plaza El Ejido s/n, 29013 M´ alaga, Spain Email: jlmartinez@uma.es, Tel: (+34) 952 131408, Fax: (+34) 952 131413 Abstract— Scene registration of 3D laser rangefinder scans is increasingly being required in applications, such as mobile robotics, that demand a timely response. For speeding up point matching methods, the large amount of range data should be reduced. This sampling, in turn, can have a significant impact on accuracy. In particular, Genetic Algorithms provide a robust optimization method that avoids local minima for scan matching, but their computational cost grows with the number of points. This paper proposes a new point sampling strategy that considers the spherical scanning process of most sensors to equalize the measure-direction density. This fast sampling method significantly reduces the number of points without loss of relevant scene information. It is experimentally compared with other systematic approaches for the case of actual scene genetic registration. I. I NTRODUCTION Scan registration can be defined as finding the translation and rotation of a projected scan contour that produces maxi- mum overlap with a reference scan or a previous model. Scan matching is a highly non-linear problem with no analytical solution that requires an initial estimation to be solved iteratively. In addition, some applications of registration with 3D laser range-finders, like mobile robotics [1], impose time constraints to this problem, in spite of the large amount of raw data to be processed. Registration of 3D scenes from laser range data is more complex than matching 2D views: • The amount of raw data is substantially bigger. • The number of degrees of freedom increases twofold. Moreover, registration of scenes is different from modeling single objects in several aspects: • The scene can have more occlusions and more invalid ranges. • The scene may contain points from unconnected re- gions. • All scan directions in the scene may contain relevant information. There are two general approaches for 3D scan registration: feature matching and point matching. The goal of feature matching is to find correspondences between singular points, edges or surfaces from range images [2]. The segmentation process used to extract and select image primitives deter- mines computation time and maximum accuracy. On the other hand, point matching techniques try to directly establish correspondences between spatial points from two views. Exact point correspondence from different scans is impossible due to a number of facts: spurious ranges, random noise, mixed pixels, occluded areas and discrete angular resolution. This is why point matching is usually regarded as an optimization problem, where the maximum expected precision is intrinsically limited by the working environment and by the rangefinder performance. Different optimization techniques have been proposed for point matching methods. They have an important computa- tional cost, which depends mainly on the number of points. Local search methods, such as Iterative Closest Point (ICP) [3] [4] and its many variants [5] or the gradient-based Levenberg-Marquardt method [6] provide faster convergence, but they can get stuck in local minima. This problem is avoided with the use of Simulated Annealing [7] or Genetic Algorithms (GA) [8], which introduce a stochastic com- ponent in the search, at the cost of even more expensive computation. Furthermore, hybrid approaches combine GA estimation with faster local refinements based on ICP [9] or hill-climbing [10]. The most straightforward approach to improve compu- tation time for point matching is to reduce the number of points to be matched. This sampling process usually has to be applied only once for each scan registration, but it can have a great impact on matching accuracy. Several point sampling methods have been proposed: • No sampling at all [3]. This is the case for 2D scan matching due to the low number of points [8] [9]. • Random sampling from raw data [11] [12]. • Uniform sampling uses equally distributed data from the scan stream [4]. • Reduction filter, where multiple close points of the same 2D scan slice are averaged into one [1]. • Mesh sampling selects vertices based on a spatial sub- division of the point cloud [13]. • Normal-space sampling selects surface points whose normal vectors are distributed uniformly in all directions [5]. • Selection of points with higher image gradients in laser reflectance measurements [14]. Randomized and uniform sampling are systematic meth-