View Planning for 3D Object Reconstruction with a Mobile Manipulator Robot Irving Vasquez, L. Enrique Sucar, Rafael Murrieta-Cid INAOE México, CIMAT México

Introduction 3D models from real objects have several uses in robotics To build a 3D model several views are required Sensor pose Robot configuration The object is not known in advance Each view is planned iteratively one by one Good morning my name is Irving Vasquez and this is a work joint with Enrique Sucar and Rafael Murrieta from Mexico. The name of this work is view planning for 3D object reconstruction with a mobile manipulator robot. 3d models from real objects have several uses in robotics, such as manipulation or motion planning. To build a 3D model several scans or views are required. Each view is planned iteratively deppending on the object. So that, the object reconstruction is performed in a cicle of four processes, positioning, sensing, registration and model update, and next best view planning. In this work we are dealing with the problem of next best view planning when a mobile manipulator is used. The figure shows our mobile manipulator during the reconstruction of a chair. Positioning Sensing Registration and update Next Best View Planning

Novelty of the Method Previous approaches compute the sensor pose, then the robot configuration. The proposed method calculates directly the robot configuration and trajectory. It is based on sampling and a fast evaluation and rejection of the samples. (x,y,z,yaw,pitch,roll) (x,y,z,α1,…,α5) 8 DOF Most of previous approaches computes the sensor pose, and then by means of inverse kinematics they determine the robot configuration. The proposed approach calculates directly the robot configuration and the robot trajectory. The method is based on a fast evaluation and rejection of a set of candidate configurations. Generation of candidates Utility Function Evaluation NBV Selection

Utility Function and Evaluation We propose a utility function And a evaluation strategy Positioning Surface and Registration Distance To discriminate between candidate configurations we use a utility function. This function has several factors that evaluate different caractererisctics that the the next-best-view must have. They are, to guarantee that the configuration is collision free, guarantee at least an overlap with previous scans in order to register the measured surface, dicover new surfaces, and try to reduce the distance with respect to the current robot configuration. To evaluate this utily function might take a lot of processing time for each canddiate. So, we developed a strategy where the factors of the utility function are applied as filters. C-space Work-space C-space

Experiments - Experiments in simulation and with a real robot with 8 DOF - Comparison versus the Information Gain approach. - Reconstruction of different complex objects We performed several experiments in simulation and with a real manipulator robot. The robot has eight degrees of freedom and has a kinect sensor mounted on the end effector. We compare the proposed utility function versus information gain. Both funtions reach the same coverage after several scans but the proposed functions requires a smaller processing time. Furthermore, we test the method in the reconstruction of several complex objects. I hope to see you at my interactive presentation.

Results Utility function reaches the same coverage that Information Gain. A high coverage is achieved.

Conclusions In our experiments, it was possible to plan the next best view directly in the configuration space using direct kinematics. For an 8 DOF robot the processing time is in the order of seconds when the evaluation strategy is applied.