FCL: A General Purpose Library for Collision and Proximity Queries

We present FCL, a new collision and proximity library that integrates several techniques for fast, accurate collision checking and proximity detection. FCL is based on hierarchical representations and is designed to perform multiple proximity queries on different model representations. The set of queries includes discrete collision detection, continuous collision detection, separation distance computation, and penetration depth estimation. The input models may correspond to triangulated rigid or deformable models and articulated models. Moreover, FCL can perform probabilistic collision checking between noisy point clouds that are captured using cameras or LIDAR sensors. The main benefit of FCL lies in the fact that it provides a unified interface that can be used by various applications. Furthermore, its flexible architecture makes it easier to implement new algorithms within this framework. The runtime performance of the library is comparable to state of the art collision and proximity algorithms. We demonstrate its performance on synthetic datasets, as well as motion planning and grasping computations performed using a two-armed mobile manipulation robot [Jia Pan, Sachin Chitta, and Dinesh Manocha].