Efficient Closed-Loop Multiple-View Registration

Abstract

Registering multiple views is an essential and challenging problem for many intelligent transportation applications that employ a mobile sensing platform or consist of multiple stationary sensors. In this paper a novel algorithm is presented for multiple-view registration under a loop closure constraint. Different from most existing methods, which use general optimization techniques, our method studies the mechanism of adjusting the poses of views in a loop and provides a highly efficient and accurate solution. We prove that translation vectors can be decoupled if the same point set is used in each view to associate the previous and subsequent views, leading to our solution for such decouplable cases. If this condition does not hold, an exact solution of translation vectors is provided when rotation parameters are given, which results in our iterative solution for general cases by updating rotation and translation alternately. In our method, the effect of the accumulated pose error in a loop can be distributed to all views efficiently through loop factors, and only a few iterations are needed. Most important of all, in each iteration our method has linear computational complexity with respect to the number of views, which is much superior to that of state-of-the-art methods. A series of experiments was conducted, involving simulation of thousands of views and real vehicle-borne sensing data that include 65 371 point pairs in 352 views. Experimental results show that our proposed method is not only stable and highly efficient but also provides competitive accuracy relative to existing methods.