The ability to find and grasp target items in an unknown environment is important for working robots. We developed an autonomous navigating and grasping robot. The operations are locating a requested item, moving to where the item is placed, finding the item on a shelf or table, and picking the item up from the shelf or the table. To achieve these operations, we designed the robot with three functions: an autonomous navigating function that generates a map and a route in an unknown environment, an item position recognizing function, and a grasping function. We tested this robot in an unknown environment. It achieved a series of operations: moving to a destination, recognizing the positions of items on a shelf, picking up an item, placing it on a cart with its hand, and returning to the starting location. The results of this experiment show the applicability of reducing the workforce with robots.
Performing efficient view frustum culling is a fundamental problem in computer graphics. In general, an octree is used
for view frustum culling. The culling checks the intersection of each octree node (cube) against the planes of the view
frustum. However, this involves many calculations. We propose a method for fast detecting the intersection of a plane
and a cube in an octree structure. When we check which child of the octree node intersects a plane, we compare the
coordinates of the corner of the node and the plane. Using an octree, we calculate the vertices of the child node by using
the vertices of the parent node. To find points within a convex region, a visibility test is performed by AND operation
with the result of three or more planes. In experiments, we tested the problem of searching for the visible point with a
camera. The method was two times faster than the conventional method, which detects a visible octree node by using the
inner product of the plane and each corner of the node.
We present a novel approach for geometric alignment of 3D sensor data. The Iterative
Closest Point (ICP) algorithm is widely used for geometric alignment of 3D models as a
point-to-point matching method when an initial estimate of the relative pose is known.
However, the accuracy of the correspondence between point and point is difficult when the
points are sparsely distributed. In addition, the searching cost is high because the ICP
algorithm requires a search of the nearest-neighbor points at every minimization. In this paper,
we describe a plane-to-plane registration method. We define the distance between two planes
and estimate the translation parameter by minimizing the distance between the planes. The
plane-to-plane method is able to register the set of scatter points which are sparsely distributed
and the density is low with low cost. We tested this method with the large scatter points of a
manufacturing plant and show the effectiveness of our proposed method.
To construct a 3D model of an environment with minimal information loss, integrated sensor data from many viewpoints are needed. However, this increases the amount of useless data, and it also takes too much time. Based on this problem, we developed a scalable sensing scheme using a robot system to reconstruct indoor environments. We mounted a freely rotating range sensor on a mobile robot to acquire range data in a real office environment, and we constructed a 3D model of that environment. The scheme determines the frequency of the measurement in each direction according to the complexity of the shape. If the shape on the direction of the sensor's angle is simple, the frequency of the measurement becomes low. On the other hand, if the shape is compleex, the frequency is high. It also does not acquire data in areas that have alreaedy been measured. The results showed that a 3D model can be constructed with less frequent measurements with our scheme.