Active 3D Classification of Multiple Objects in Cluttered Scenes

Abstract

Autonomous agents that need to effectively move and interact in a realistic environment have to be endowed with robust perception skills. Among many, accurate object classification is an essential supporting element for assistive robotics. However, realistic scenarios often present scenes with severe clutter, that dramatically degrades the performance of current object classification methods. This paper presents an active vision approach that improves the accuracy of 3D object classification through a next-best-view (NBV) paradigm to perform this complex task with ease. The next camera motion is chosen with the criteria that aim to avoid object self-occlusions while exploring as much as possible the surrounding area. An online 3D reconstruction module is exploited in our system in order to obtain a better canonical 3D representation of the scene while moving the sensor. By reducing the impact of occlusions, we show with both synthetic and real-world data that in a few moves the approach can surpass a state-of-the-art method, PointNet with single view object classification from depth data. In addition, we demonstrate our system in a practical scenario where depth sensor moves to search and classify a set of objects in cluttered scenes.