Estimating multimodal attributes for unknown objects

If a robot is expected to perform in the real-world, the robot should recognize objects in such environment using its multimodal sensors in real-time. Traditional multimodal object classification methods focus on recognizing known objects; however, it is impossible to learn all objects that we use. On the other hand, the classification of unknown objects has become a popular topic in image processing. However popular methods have batch algorithms, and there is no method to integrate multimodal classification results with an online algorithm. This study proposes a novel method that estimates multimodal attributes of an unknown object. The method uses an ultra-fast and online learning method based on a STAR-SOINN, which stands for STAtistical Recognition on Self-Organizing and Incremental Neural Network. The results from a comparative experiment show that the recognition accuracy for known objects is higher than a method that naïvely integrates the modalities and a previous method. And this method works very quickly: approximately 1 second to learn one object, and 25 millisecond for a single estimation. We also conducted an experiment to estimate attributes of unknown objects, it could estimate approximately 90% of the attributes for these objects.