In a rescue operation walking robots offer a great deal of flexibility in traversing uneven terrain in an uncontrolled environment. For such a rescue robot each motion is a potential vital sign but the existing techniques for motion detection have severe limitations in dealing with strong levels of ego-motion on walking robots. This paper proposes an optical flow based method for the detection of moving objects using a single camera mounted on a hexapod robot for an application in a rescue scenario. The proposed algorithm estimates and compensates ego-motion to allow for object detection while the robot is moving. Our algorithm can deal with strong rotation and translation in 3D, using a first-order-flow motion model, with four degrees of freedom. Two alternative object detection methods using a 2D-histogram based vector clustering and motion compensated frame differencing respectively are examined for the detection of slow and fast moving objects. In addition to a software implementation, the system was implemented on an FPGA, enabling processing in real-time at 31 fps.
|Title of host publication||2010 IEEE Safety Security and Rescue Robotics|
|Number of pages||8|
|Publication status||Published - 01.12.2010|
|Event||8th IEEE International Workshop on Safety, Security, and Rescue Robotics |
- Bremen, Germany
Duration: 26.07.2010 → 30.07.2010
Conference number: 86439