In this paper we introduce a system for unsupervised object discovery and segmentation of RGBD-images. The system models the sensor noise directly from data, allowing accurate segmentation without sensor specific hand tuning of measurement noise models making use of the recently introduced Statistical Inlier Estimation (SIE) method. Through a fully probabilistic formulation, the system is able to apply probabilistic inference, enabling reliable segmentation in previously challenging scenarios. In addition, we introduce new methods for filtering out false positives, significantly improving the signal to noise ratio. We show that the system significantly outperform state-of-the-art in on a challenging real-world dataset.
@cite_14 a system for detecting changes in NDT representations based on color images and lidar range scanners is presented.
@cite_14
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This paper presents a system for autonomous change detection with a security patrol robot. In an initial step a reference model of the environment is created and changes are then detected with respect to the reference model as differences in coloured 3D point clouds, which are obtained from a 3D laser range scanner and a CCD camera. The suggested approach introduces several novel aspects, including a registration method that utilizes local visual features to determine point correspondences (thus essentially working without an initial pose estimate) and the 3D-NDT representation with adaptive cell size to efficiently represent both the spatial and colour aspects of the reference model. Apart from a detailed description of the individual parts of the difference detection system, a qualitative experimental evaluation in an indoor lab environment is presented, which demonstrates that the suggested system is able register and detect changes in spatial 3D data and also to detect changes that occur in colour space and are not observable using range values only. In this letter, a novel approach that utilizes the spectrum information (i.e., images) provided in a modern light detection and ranging (LiDAR) sensor is proposed for the registration of multistation LiDAR data sets. First, the conjugate points in the images collected at varied LiDAR stations are extracted through the speedup robust feature technique. Then, by applying the image-object space mapping technique, the 3-D coordinates of the conjugate image points can be obtained. Those identified 3-D conjugate points are then fed into a registration model so that the transformation parameters can be immediately solved using the efficient noniterative solution to linear transformations technique. Based on numerical results from a case study, it has been demonstrated that, by implementing the proposed approach, a fully automatic and reliable registration of multistation LiDAR point clouds can be achieved without the need for any human intervention. We propose a new approach to appearance based loop detection from metric 3D maps, exploiting the NDT surface representation. Locations are described with feature histograms based on surface orientation and smoothness, and loop closure can be detected by matching feature histograms. We also present a quantitative performance evaluation using two real-world data sets, showing that the proposed method works well in different environments. This paper examines the problem of detecting changes in a 3-d scene from a sequence of images, taken by cameras with arbitrary but known pose. No prior knowledge of the state of normal appearance and geometry of object surfaces is assumed, and abnormal changes can occur in any image of the sequence. To the authors' knowledge, this paper is the first to address the change detection problem in such a general framework. Existing change detection algorithms that exploit multiple image viewpoints typically can detect only motion changes or assume a planar world geometry which cannot cope effectively with appearance changes due to occlusion and un-modeled 3-d scene geometry (ego-motion parallax). The approach presented here can manage the complications of unknown and sometimes changing world surfaces by maintaining a 3-d voxel-based model, where probability distributions for surface occupancy and image appearance are stored in each voxel. The probability distributions at each voxel are continuously updated as new images are received. The key question of convergence of this joint estimation problem is answered by a formal proof based on realistic assumptions about the nature of real world scenes. A series of experiments are presented that evaluate change detection accuracy under laboratory-controlled conditions as well as aerial reconnaissance scenarios.-