Global registration of mid-range 3D observations and short range next best views

Abstract

This work proposes a method for autonomous robot exploration of unknown objects by sensor fusion of 3D range data. The approach aims at overcoming the physical limitation of the minimum sensing distance of range sensors. Two range sensors are used with complementary characteristics mounted in eye-in-hand configuration on a robot arm. The first sensor operates at mid-range and is used in the initial phase of exploration when the environment is unknown. The second sensor, which provides short-range data, is used in the following phase where the objects are explored at close distance through next best view planning. Next best view planning is performed using a volumetric representation of the environment. A complete point cloud model of each object is finally computed by global registration of all object observations including mid-range and short range views. In experiments performed in environments with multiple rigid objects the global registration algorithm has proven more accurate than a standard sequential registration approach.