Abstract: In order to ensure safe and accurate landing on celestial bodies, it is necessary to measure the speed of space probe, providing important information for guidance, navigation and control system. A real time visual velocity measurement method only based on optical camera is proposed in this paper. For terrain image sequences of celestial bodies, we use recurrent all pairs field transforms to extract the optical flow field between adjacent frames. Then we extract the eigenvectors corresponding to the optical flow field via the convolution layer and pooling layer in deep neural networks. To reduce the influence on measurement accuracy caused by visual perspective during the landing process, a long short term memory network for video sequences is constructed to match the eigenvectors up with velocities, thus a real time landing speed estimation is achieved for space probes. Simulation results demonstrate that our technique decreases the mean absolute percentage error by 11.98% and has higher measurement accuracy in comparison to the forward propagation network.

Key words: landing velocity measurement, optical flow field, deep neural network, celestial body exploration, guidance, navigation and control