GLOBAL REGISTRATION OF KINECT POINT CLOUDS USING AUGMENTED EXTENDED INFORMATION FILTER AND MULTIPLE FEATURES
Because the Infra-Red (IR) Kinect sensor only provides accurate depths up to 5 m for a limited field of view (60°), the problem of registration error accumulation becomes inevitable in indoor mapping. Therefore, in this paper, a global registration method is proposed based on augmented extended Information Filter (AEIF). The point cloud registration is regarded as a stochastic system so that AEIF is used to produces the accurate estimates of rigid transformation parameters through eliminating the error accumulation suffered by the pair-wise registration. Moreover, because the indoor scene normally contains planar primitives, they can be employed to control the registration of multiple scans. Therefore, the planar primitives are first fitted based on optimized BaySAC algorithm and simplification algorithm preserving the feature points. Besides the constraint of corresponding points, we then derive the plane normal vector constraint as an additional observation model of AEIF to optimize the registration parameters between each pair of adjacent scans. The proposed approach is tested on point clouds acquired by a Kinect camera from an indoor environment. The experimental results show that our proposed algorithm is proven to be capable of improving the accuracy of multiple scans aligning by 90%.