Prototype Deep Learning System for Terrain Relative Navigation Missions

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 3

Year of Publication : 2023

Authors : B. Saravana Kumar, Nagendra Gajjar

10.14445/23488379/IJEEE-V10I3P105

How to Cite?

B. Saravana Kumar, Nagendra Gajjar, "Prototype Deep Learning System for Terrain Relative Navigation Missions," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 3, pp. 44-51, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I3P105

Abstract:

Navigation of a lander craft to accurately soft land in a predetermined spot on an extra-terrestrial body is a challenging task. Due to the absence of GPS and real-time ground communication links, the lander craft has to navigate autonomously to the targeted landing site solely with the help of its onboard sensors. One of the major challenges in position estimation of the lander craft is the measurement inaccuracies associated with the inertial navigation systems. Optical landmark detection-based techniques are employed to aid the lander navigation and guidance system. In this technique, craters and their relative positions are used as landmarks for position estimation. Typical crater detection algorithms employ handcrafted feature extractors for crater identification. Recently deep learning-based approaches have been employed for crater detection, mainly for geomorphological studies and cataloguing. This paper proposes using deep learning-based object detection techniques applied to autonomous landing missions and their implementation on a Xilinx MPSoC FPGA device. Prototype hardware has been developed, and the YOLOv4-tiny algorithm has been implemented on it with an inference time of 471 ms per image. The results are presented, and their performance is evaluated.

Keywords:

Crater detection, CNN, Deep learning, FPGA, Object detection, YOLO.

References:

[1] Yang Cheng et al., “Optical Landmark Detection for Spacecraft Navigation,” AAS/AIAA, Astrodynamics Specialist Conference, 2003.
Google Scholar | Publisher Link
[2] Adnan Ansar, and Yang Chen, “Vision Technologies for Small Body Proximity Operations,” i-SAIRAS 2010, 2010.
Google Scholar | Publisher Link
[3] Nikolas Trawny et al., “Visual Aided Inertial Navigation for Pin Point Navigation using Observations of Mapped Landmarks,” Special Issue on Space Robotics PART-III, pp. 357-378, 2004.
Google Scholar | Publisher Link
[4] Bach Van Pham, Simon Lacroix, and Michel Devy, “Visual Landmark Constellation matching for Spacecraft Pinpoint Landing,” AIAA Guidance, Navigation and Control Conference, 2009.
Google Scholar | Publisher Link
[5] Min Chen et al., “Lunar Crater Detection Based on Terrain Analysis and Mathematical Morphology Methods Using Digital Elevation Models,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no.7, pp. 3681-3692, 2018.
CrossRef | Google Scholar | Publisher Link
[6] Andrew E. Johnson, and James F. Montgomery, "Overview of Terrain Relative Navigation Approaches for Precise Lunar Landing," 2008 IEEE Aerospace Conference, Big Sky, MT, USA, pp. 1-10, 2008.
CrossRef | Google Scholar | Publisher Link
[7] Yunfeng Yan, Donglian Qi, and Chaoyong Li, “Vision-Based Crater and Rock Detection Using a Cascade Decision Forest,” IET Computer Vision, vol. 13, no. 6, pp. 549-555, 2019.
CrossRef | Google Scholar | Publisher Link
[8] Ari Silburt et al., “Lunar Crater Identification via Deep Learning,” Icarus, vol. 317, pp. 27-38, 2019.
CrossRef | Google Scholar | Publisher Link
[9] Christopher Lee, and James Hogan, “Automated Crater Detection with Human-Level Performance,” Computers & Geosciences, vol. 147, 2021.
CrossRef | Google Scholar | Publisher Link
[10] D. Sheema et al., "An Algorithm for Detection and Identification of Infestation Density of Pest-Fall Armyworm in Maize Plants using Deep Learning based on IoT," International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 240-251, 2022.
CrossRef | Google Scholar | Publisher Link
[11] Zhengxia Zou et al., “Object Detection in 20 Years: A Survey,” Proceedings of IEEE, vol. 111, no. 3, pp. 257-276, 2023.
CrossRef | Google Scholar | Publisher Link
[12] Licheng Jiao et al., “A Survey of Deep Learning-Based Object Detection,” IEEE Access, vol. 7, pp. 128837–128868, 2019.
CrossRef | Google Scholar | Publisher Link
[13] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, "Imagenet Classification with Deep Convolutional Neural Networks," Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.
CrossRef | Google Scholar | Publisher Link
[14] Ross Girshick et al., “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, 2014.
CrossRef | Google Scholar | Publisher Link
[15] Ross Girshick et al., "Fast R-CNN," Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448, 2015.
CrossRef | Google Scholar | Publisher Link
[16] Shaoqing Ren et al., "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.
CrossRef | Google Scholar | Publisher Link
[17] Kaiming He et al., "Mask R-CNN," IEEE International Conference on Computer Vision, pp. 2980-2988, 2017.
CrossRef | Google Scholar | Publisher Link
[18] Joseph Redmon et al., “You Only Look Once: Unified, Real-Time Object Detection,” CVPR, 2016.
Google Scholar | Publisher Link
[19] Ramya R, and Madhura R, "FPGA Implementation of Optimized BIST Architecture for Testing of Logic Circuits," SSRG International Journal of VLSI & Signal Processing, vol. 7, no. 2, pp. 36-42, 2020.
CrossRef | Publisher Link
[20] Shanyong Xu et al., “YOLOv4-Tiny-Based Coal Gangue Image Recognition and FPGA Implementation,” Micromachines, vol. 13, 2022.
CrossRef | Google Scholar | Publisher Link
[21] Sergio Saponara1 et al., “Developing a Real-Time Social Distancing Detection System Based on Yolov4-Tiny and Bird-Eye View for COVID-19,” Journal of Real-Time Image Processing, vol. 19, pp. 551–563, 2022.
CrossRef | Google Scholar | Publisher Link
[22] [Online]. Available: https://github.com/AlexeyAB/Yolo_mark 
[23] [Online]. Available: https://github.com/AlexeyAB/darknet
[24] [Online]. Available: https://colab.research.google.com
[25] [Online]. Available: https://lroc.asu.edu/about
[26] L. H. Crockett et al., Exploring Zynq MPSoC: With PYNQ and Machine Learning Applications, Strathclyde Academic Media, 2019.
Google Scholar | Publisher Link
[27] [Online]. Available: https://www.xilinx.com/products/silicon-devices/soc/zynq-ultrascale-mpsoc.html
[28] S. Bhuvaneswari et al., "Disease Detection in Plant Leaf using LNet Based on Deep Learning," International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 64-75, 2022.
CrossRef | Publisher Link
[29] Y. Sawabe, T. Matsunaga, and S. Rokugawa, “Automated Detection and Classification of Lunar Craters Using Multiple Approaches,” Advances in Space Research, vol. 37, no. 1, pp. 21-27, 2006.
CrossRef | Google Scholar | Publisher Link
[30] Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,”     arXiv.2004.10934, 2020.
CrossRef | Google Scholar | Publisher Link
[31] Ms.Manjula B.M, and Dr.Chirag Sharma, "FPGA Implementation of BCG Signal Filtering Scheme by using Weight Update Process," SSRG International Journal of VLSI & Signal Processing, vol. 3, no. 3, pp. 1-7, 2016.
CrossRef | Google Scholar | Publisher Link