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With regard to performance well established SW-only design methodologies proceed by making the initial specification run first, then by enhancing its functionality and finally by optimizing it. When designing Embedded Systems (EbS) this approach is not viable since decisive design decisions like e.g. the estimation of required processing power or the identification of those parts of the specification which need to be delegated to dedicated HW depend on the fastness and fairness of the initial specification. We here propose a sequence of optimization steps embedded into the design flow, which enables a structured way to accelerate a given working EbS specification at different layers. This sequence of accelerations comprises algorithm selection, algorithm transformation, data transformation, implementation optimization and finally HW acceleration. It is analyzed how all acceleration steps are influenced by the specific attributes of the underlying EbS. The overall acceleration procedure is explained and quantified at hand of a real-life industrial example.
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.