Working with Twitter and Double Robotics, The Post’s robot will provide a live stream of delegates and politicians in Cleveland and Philadelphia via Twitter’s app, Periscope, giving users a guided tour of the convention site and letting them ask questions about the convention experience via Periscope chat.
CSAIL says Barry’s software runs 20 times faster than existing obstacle detection software. Operating at 120 frames per second, the open-source software allows the drone to detect objects and map its environment in real time, extracting depth information at 8.3 milliseconds per frame.
Barry wrote about the system in his paper “Pushbroom Stereo for High-Speed Navigation in Cluttered Environments” (PDF) and says he needs to improve the software so it can work at more than one depth and dense environments.
Because a SLAM map is three-dimensional, however, it does a better job of distinguishing objects that are near each other than single-perspective analysis can. The system devised by Pillai and Leonard, a professor of mechanical and ocean engineering, uses the SLAM map to guide the segmentation of images captured by its camera before feeding them to the object-recognition algorithm. It thus wastes less time on spurious hypotheses.
More important, the SLAM data let the system correlate the segmentation of images captured from different perspectives. Analyzing image segments that likely depict the same objects from different angles improves the system’s performance.
Source: Object recognition for robots
The great promise of this kind of algorithm is in image search. At the moment, it is straightforward to hunt for images taken at a specific place or at a certain time. But it is hard to find images taken of specific people. This is step in that direction. It is inevitable that this capability will be with us in the not too distant future.
In this research we develop a janken (rock-paper-scissors) robot with 100% winning rate as one example of human-machine cooperation systems. Human being plays one of rock, paper and scissors at the timing of one, two, three. According to the timing, the robot hand plays one of three kinds so as to beat the human being.
Recognition of human hand can be performed at 1ms with a high-speed vision, and the position and the shape of the human hand are recognized. The wrist joint angle of the robot hand is controlled based on the position of the human hand. The vision recognizes one of rock, paper and scissors based on the shape of the human hand. After that, the robot hand plays one of rock, paper and scissors so as to beat the human being in 1ms.