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Nearest Neighbor Search (NNS) is employed by many computer vision algorithms. The computational complexity is large and constitutes a challenge for real-time capability. The basic problem is in rapidly processing a huge amount of data, which is often addressed by means of highly sophisticated search methods and parallelism. We show that NNS based vision algorithms like the Iterative Closest Points algorithm (ICP) can achieve real-time capability while preserving compact size and moderate energy consumption as it is needed in robotics and many other domains. The approach exploits the concept of general purpose computation on graphics processing units (GPGPU) and is compared to parallel processing on CPU. We apply this approach to the 3D scan registration problem, for which a speed-up factor of 88 compared to a sequential CPU implementation is reported.
This paper presents an approach to estimate theego-motion of a robot while moving. The employed sensor is aTime-of-Flight (ToF) camera, the SR3000 from Mesa Imaging.ToF cameras provide depth and reflectance data of the scene athigh frame rates.The proposed method utilizes the coherence of depth andreflectance data of ToF cameras by detecting image features onreflectance data and estimating the motion on depth data. Themotion estimate of the camera is fused with inertial measure-ments to gain higher accuracy and robustness.The result of the algorithm is benchmarked against referenceposes determined by matching accurate 2D range scans. Theevaluation shows that fusing the pose estimate with the datafromthe IMU improves the accuracy and robustness of the motionestimate against distorted measurements from the sensor.