Abstract
This paper estimates the pose of a noncooperative space target utilizing a direct method of monocular visual simultaneous location and mapping (SLAM). A Large Scale Direct SLAM (LSD-SLAM) algorithm for pose estimation based on photometric residual of pixel intensities is provided to overcome the limitation of existing feature-based on-orbit pose estimation methods. Firstly, new sequence images of the on-orbit target are continuously inputted, and the pose of each current frame is calculated according to minimizing the photometric residual of pixel intensities. Secondly, frames are distinguished as keyframes or normal frames according to the pose relationship, and these frames are used to optimize the local map points. After that, the optimized local map points are added to the back-end map. Finally, the poses of keyframes are further enumerated and optimized in the back-end thread based on the map points and the photometric residual between the keyframes. Numerical simulations and experiments are carried out to prove the validity of the proposed algorithm, and the results elucidate the effectiveness of the algorithm in estimating the pose of the noncooperative target.
Highlights
With the development of space technology, on-orbit service is getting widespread attentions
The space targets can be divided into cooperative targets and noncooperative targets
In order to indicate the estimation accuracy of kinematic information in different roughness conditions on the surface of the object, this paper considers three kinds of satellite models with different surface texture features and different amounts of the components, as shown in Figure 4, which are (a) no texture on the surface and 3 components mounted on the model, (b) rough textures on the surface and 3 components mounted on the model, and (c) rough textures on the surface and 5 components mounted on the model
Summary
With the development of space technology, on-orbit service is getting widespread attentions. Shtark and Gurfil [13] developed a computer-vision feature detection and matching algorithm to identify and locate the target in the captured images and designed three different filters to estimate the relative position and velocity He et al [14] proposed a measurement method of relative position and attitude between two noncooperative spacecrafts based on graph cut and edge information algorithm. Due to the direct use of image intensity, direct SLAM does not depend on the number of features in the scene, so it has strong robustness under complex conditions such as occlusion and weak texture scenes and is not affected by such factors It meets the requirements of real time and accuracy.
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