Abstract
Visual navigation is an essential part of planetary rover autonomy. Rock segmentation emerged as an important interdisciplinary topic among image processing, robotics, and mathematical modeling. Rock segmentation is a challenging topic for rover autonomy because of the high computational consumption, real-time requirement, and annotation difficulty. This research proposes a rock segmentation framework and a rock segmentation network (NI-U-Net++) to aid with the visual navigation of rovers. The framework consists of two stages: the pre-training process and the transfer-training process. The pre-training process applies the synthetic algorithm to generate the synthetic images; then, it uses the generated images to pre-train NI-U-Net++. The synthetic algorithm increases the size of the image dataset and provides pixel-level masks—both of which are challenges with machine learning tasks. The pre-training process accomplishes the state-of-the-art compared with the related studies, which achieved an accuracy, intersection over union (IoU), Dice score, and root mean squared error (RMSE) of 99.41%, 0.8991, 0.9459, and 0.0775, respectively. The transfer-training process fine-tunes the pre-trained NI-U-Net++ using the real-life images, which achieved an accuracy, IoU, Dice score, and RMSE of 99.58%, 0.7476, 0.8556, and 0.0557, respectively. Finally, the transfer-trained NI-U-Net++ is integrated into a planetary rover navigation vision and achieves a real-time performance of 32.57 frames per second (or the inference time is 0.0307 s per frame). The framework only manually annotates about 8% (183 images) of the 2250 images in the navigation vision, which is a labor-saving solution for rock segmentation tasks. The proposed rock segmentation framework and NI-U-Net++ improve the performance of the state-of-the-art models. The synthetic algorithm improves the process of creating valid data for the challenge of rock segmentation. All source codes, datasets, and trained models of this research are openly available in Cranfield Online Research Data (CORD).
Highlights
Planetary rovers integrate various sensors and computing units, making the study an interdisciplinary research topic of subjects such as mathematics, human–robot interaction, and computer vision [1,2,3]
The proposed rock segmentation framework is based on the transfer learning process
Directly embedding a rock sample extracted from a dark region of 28 to a bright region of the real-life background is not visually comfortable
Summary
Planetary rovers integrate various sensors and computing units, making the study an interdisciplinary research topic of subjects such as mathematics, human–robot interaction, and computer vision [1,2,3]. The Spirit rover endured the Martian winter, survived. 1000 Martian days (sols), and traveled more than 6876 m, while the Opportunity rover traveled more than 9406 m [4]. The space environment poses challenges to the planetary rover operation [5]. The Spirit and Opportunity rovers experienced communication and function failures during their explorations [6,7]. To prevent this, automating onboard systems is essential for future planetary rovers [3,8]. This research focuses on the semantic terrain segmentation from the monocular navigation vision of the planetary rovers [8], which can provide support for the high-level planetary rover functionalities
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