We introduce a simple yet effective algorithm that uses convolutional neural networks to directly estimate object poses from videos. Our approach leverages the temporal information from a video sequence, and is computationally efficient and robust to support robotic and AR domains. Our proposed network takes a pre-trained 2D object detector as input, and aggregates visual features through a recurrent neural network to make predictions at each frame. Experimental evaluation on the YCB-Video dataset show that our approach is on par with the state-of-the-art algorithms. Further, with a speed of 30 fps, it is also more efficient than the state-of-the-art, and therefore applicable to a variety of applications that require real-time object pose estimation.
PISAT is a nano-satellite currently under development at PES University, Bangalore. It is an imaging satellite with the GOMSPACE Nanocam C1U as its main payload. It is three axis stabilized with active magnetic control system. Data reception and transmission is through S-band communication system. PES ground station has been commissioned exclusively for the purpose of communicating with PISAT. In the present configuration of PISAT, imaging can be carried out only during ground station visibility, which is for approximately 15 minutes. The dedicated ground station being located at Bangalore, the satellite can thus capture images only over Bangalore in this current mode. However, it is desirable that PISAT be able to carry out imaging at any commanded latitude and longitude. This paper presents a method to add this capability of imaging anywhere by including a provision to estimate the time required to reach the desired latitude and longitude using Location-Based Payload Imaging.
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