point cloud based object recognition in living...

HSR Pepper

3D sensor

The service robots

Decrease

Point Cloud Based Object Recognition in Living Environment○Takayuki Fujimoto, Takeshi Nishida, Kyushu Institute of Technology

Object Detection

BackgroundPoint Cloud

Processing method

Extraction of regionof interest

Landscape(Robot workplace)

Point Cloud Backgroundremoving

Clustering Calculation of surface normal vectors

Object Recognition

K-meansClustering

Test data Advance data

(a)Ball(b)Column(c)Cube

Evaluation function Result

Normal information

Original normal vectors

Clustering Reduced normalvectors

Matching

Cube・Normal vector templates

𝑈 = 𝒖𝑚 𝑚 = 1,⋯ ,𝑀 = 128

・Weight of direction

𝜂𝑚(𝑓): 𝑓 = Ball, Column, Cube

The service robots are equipping multi sensors, such as laser range finder and cameras, for the environment measurement. It can obtain data called point cloud by using 3D sensor. It is expected that it can be used for object recognition and object grasping.

In general, information size of the normal vectors of PC is large, and the calculation cost of the recognition procedure becomes a bottleneck of real time processing of the robot.

・Collection of characteristic vectors 𝐷(𝑗) = 𝒅𝑙(𝑗)|𝑙 = 1,⋯ ,𝐾

・Weight of number of surface normal vectors 𝜀𝑙(𝑗)

𝐸 𝑗 = 𝑙=1

𝐾

𝜀𝑙𝑗𝜂𝑚𝑓min𝒖𝑚cos−1 𝒅𝑙

𝑗𝒖𝑚

Search of the shape that minimizes 𝐸(𝑗)

Characteristic vectors

Correct answers Accuracy rate [%] Computation time per frame [s]

10 96 80.00 0.231

20 110 91.66 0.27

30 114 95.00 0.347

Table 1 Correct recognition ratio vs. the number of Characteristic vectors

Experiment

chips teacup box1 box2 tissue ball

Cylinder Cylinder Box Box Box Ball

Determined using evaluation function