We address the problem of detecting complex articulated objects and their pose in 3D range scan data. This task is very difficult when the orientation of the object is unknown, and occlusion and clutter are present in the scene. To address the problem, we design an efficient probabilistic framework, based on the articulated model of an object, which combines multiple information sources. Our framework enforces that the surfaces and edge discontinuities of model parts are matched well in the scene while respecting the rules of occlusion, that joint constraints and angles are maintained, and that object parts don?t intersect. Our approach starts by using low-level detectors to suggest part placement hypotheses. In a hypothesis enrichment phase, these original hypotheses are used to generate likely placement suggestions for their neighboring parts. The probabilities over the possible part placement configurations are computed using efficient OpenGL rendering. Loopy belief propagation is used to optimize the resulting Markov network to obtain the most likely object configuration, which is additionally refined using an Iterative Closest Point algorithm adapted for articulated models. Our model is tested on several datasets, where we demonstrate successful pose detection for models consisting of 15 parts or more, even when the object is seen from different viewpoints, and various occluding objects and clutter are present in the scene.