SuperNoVA: Algorithm-Hardware Co-Design for Resource-Aware SLAM
Pages 1035 - 1051
Abstract
Simultaneous Localization and Mapping (SLAM) plays a crucial role in robotics, autonomous systems, and augmented and virtual reality (AR/VR) applications by enabling devices to understand and map unknown environments. However, deploying SLAM in AR/VR applications poses significant challenges, including the demand for high accuracy, real-time processing, and efficient resource utilization, especially on compact and lightweight devices. To address these challenges, we propose SuperNoVA, which enables high-accuracy, real-time, large-scale SLAM in resource-constrained settings through a full-stack system, spanning from algorithm to hardware. In particular, SuperNoVA dynamically constructs a subgraph to meet the latency target while preserving accuracy, virtualizes hardware resources for efficient graph processing, and implements a novel hardware architecture to accelerate the SLAM backend efficiently. Evaluation results demonstrate that, for a large-scale AR dataset, SuperNoVA reduces full SLAM backend computation latency by 89.5% compared to the baseline out-of-order CPU and 78.6% compared to the baseline embedded GPU, and reduces the maximum pose error by 89% over existing SLAM solutions, while always meeting the latency target.
References
[1]
Sameer Agarwal, Keir Mierle, and The Ceres Solver Team. Ceres Solver, 3 2022.
[2]
Stefano Aldegheri, Nicola Bombieri, Domenico D. Bloisi, and Alessandro Farinelli. Data flow orb-slam for real-time performance on embedded gpu boards. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5370--5375, 2019.
[3]
Alon Amid, David Biancolin, Abraham Gonzalez, Daniel Grubb, Sagar Karandikar, Harrison Liew, Albert Magyar, Howard Mao, Albert Ou, Nathan Pemberton, Paul Rigge, Colin Schmidt, John Wright, Jerry Zhao, Yakun Sophia Shao, Krste Asanović, and Borivoje Nikolić. Chipyard: Integrated design, simulation, and implementation framework for custom socs. IEEE Micro, 40(4):10--21, 2020.
[4]
ARM. ARM Cortex-A72. Accessed: 2023-12-01.
[5]
ARM. ARM NEON SIMD. Accessed: 2023-12-01.
[6]
Jonathan Bachrach, Huy Vo, Brian Richards, Yunsup Lee, Andrew Waterman, Rimas Avižienis, John Wawrzynek, and Krste Asanović. Chisel: Constructing hardware in a scala embedded language. In Proceedings of the 49th Annual Design Automation Conference, DAC '12, page 1216--1225, New York, NY, USA, 2012. Association for Computing Machinery.
[7]
Armand Behroozi, Yuxiang Chen, Vlad Fruchter, Subramanian, Lavanya Subramanian, Sriseshan Srikanth, and Scott Mahlek. Slimslam: An adaptive runtime for visual-inertial simultaneous localization and mapping. In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS '24, 2024.
[8]
Michael Bloesch, Sammy Omari, Marco Hutter, and Roland Siegwart. Robust visual inertial odometry using a direct ekf-based approach. In 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), pages 298--304. IEEE, 2015.
[9]
Michael Burri, Janosch Nikolic, Pascal Gohl, Thomas Schneider, Joern Rehder, Sammy Omari, Markus W Achtelik, and Roland Siegwart. The euroc micro aerial vehicle datasets. The International Journal of Robotics Research, 2016.
[10]
Cesar Cadena, Luca Carlone, Henry Carrillo, Yasir Latif, Davide Scaramuzza, Jose Neira, Ian Reid, and John J. Leonard. Past, present, and future of simultaneous localization and mapping: Toward the robustperception age. Trans. Rob., page 1309--1332, 2016.
[11]
Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M. M. Montiel, and Juan D. Tardós. Orb-slam3: An accurate open-source library for visual, visual--inertial, and multimap slam. IEEE Transactions on Robotics, 37(6):1874--1890, 2021.
[12]
Luca Carlone, Roberto Tron, Kostas Daniilidis, and Frank Dellaert. Initialization techniques for 3d slam: A survey on rotation estimation and its use in pose graph optimization. In 2015 IEEE international conference on robotics and automation (ICRA), pages 4597--4604. IEEE, 2015.
[13]
NVIDIA corporation. cuvslam, 2023.
[14]
Frank Dellaert and GTSAM Contributors. borglab/gtsam, May 2022.
[15]
I. S. Duff and J. K. Reid. The multifrontal solution of indefinite sparse symmetric linear. ACM Trans. Math. Softw., 9(3):302--325, sep 1983.
[16]
Axel Feldmann and Daniel Sanchez. Spatula: A hardware accelerator for sparse matrix factorization. In Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO '23, page 91--104, 2023.
[17]
Y. Gan, Y. Bo, B. Tian, L. Xu, W. Hu, S. Liu, Q. Liu, Y. Zhang, J. Tang, and Y. Zhu. Eudoxus: Characterizing and accelerating localization in autonomous machines industry track paper. In 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA), pages 827--840, 2021.
[18]
Hasan Genc, Seah Kim, Alon Amid, Ameer Haj-Ali, Vighnesh Iyer, Pranav Prakash, Jerry Zhao, Daniel Grubb, Harrison Liew, Howard Mao, Albert Ou, Colin Schmidt, Samuel Steffl, John Wright, Ion Stoica, Jonathan Ragan-Kelley, Krste Asanovic, Borivoje Nikolic, and Yakun Sophia Shao. Gemmini: Enabling systematic deep-learning architecture evaluation via full-stack integration. In Design Automation Conference, pages 769--774, 2021.
[19]
Giorgio Grisetti, Rainer Kümmerle, Hauke Strasdat, and Kurt Konolige. g2o: A general framework for (hyper) graph optimization. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages 9--13, 2011.
[20]
Michael Grupp. evo: Python package for the evaluation of odometry and slam. https://github.com/MichaelGrupp/evo, 2017.
[21]
Ramyad Hadidi, Bahar Asgari, Sam Jijina, Adriana Amyette, Nima Shoghi, and Hyesoon Kim. Quantifying the design-space tradeoffs in autonomous drones. ASPLOS '21, page 661--673, 2021.
[22]
Yuhui Hao, Yiming Gan, Bo Yu, Qiang Liu, Yinhe Han, Zishen Wan, and Shaoshan Liu. Orianna: An accelerator generation framework for optimization-based robotic applications. In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, page 813--829, 2024.
[23]
Joel Hesch, Anna Kozminski, and Oskar Linde. Powered by ai: Oculus insight, 2019.
[24]
Guoquan P Huang, Anastasios I Mourikis, and Stergios I Roumeliotis. An observability-constrained sliding window filter for slam. In 2011 IEEE/RSJ international conference on intelligent robots and systems, pages 65--72. IEEE, 2011.
[25]
Muhammad Huzaifa, Rishi Desai, Samuel Grayson, Xutao Jiang, Ying Jing, Jae Lee, Fang Lu, Yihan Pang, Joseph Ravichandran, Finn Sinclair, Boyuan Tian, Hengzhi Yuan, Jeffrey Zhang, and Sarita V. Adve. Exploring extended reality with illixr: A new playground for architecture research, 2021.
[26]
Michael Kaess, Hordur Johannsson, Richard Roberts, Viorela Ila, John J Leonard, and Frank Dellaert. isam2: Incremental smoothing and mapping using the bayes tree. The International Journal of Robotics Research, 31(2):216--235, 2012.
[27]
S. Karandikar, H. Mao, D. Kim, D. Biancolin, A. Amid, D. Lee, N. Pemberton, E. Amaro, C. Schmidt, A. Chopra, Q. Huang, K. Kovacs, B. Nikolic, R. Katz, J. Bachrach, and K. Asanovic. Firesim: Fpgaaccelerated cycle-exact scale-out system simulation in the public cloud. In 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), pages 29--42, 2018.
[28]
Seah Kim, Hasan Genc, Vadim Vadimovich Nikiforov, Krste Asanovic, Borivoje Nikolic, and Yakun Sophia Shao. Moca: Memory-centric, adaptive execution for multi-tenant deep neural networks. In International Symposium on High-Performance Computer Architecture (HPCA), pages 828--841, 2023.
[29]
Seah Kim, Jerry Zhao, Krste Asanovic, Borivoje Nikolic, and Yakun Sophia Shao. Aurora: Virtualized accelerator orchestration for multi-tenant workloads. In Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO '23, page 62--76, 2023.
[30]
Seah Kim, Jerry Zhao, Krste Asanovic, Borivoje Nikolic, and Yakun Sophia Shao. Aurora: A full-stack solution for scalable and virtualized accelerator integration. IEEE Micro, 44(4):97--105, 2024.
[31]
Eugenia M Kolasinski. Simulator sickness in virtual environments. 1995.
[32]
John J Leonard and Hugh F Durrant-Whyte. Simultaneous map building and localization for an autonomous mobile robot. In IROS, volume 3, pages 1442--1447, 1991.
[33]
Ziyun Li, Yu Chen, Luyao Gong, Lu Liu, Dennis Sylvester, David Blaauw, and Hun-Seok Kim. An 879gops 243mw 80fps vga fully visual cnn-slam processor for wide-range autonomous exploration. In 2019 IEEE International Solid-State Circuits Conference - (ISSCC), pages 134--136, 2019.
[34]
Joseph W Liu. A compact row storage scheme for cholesky factors using elimination trees. ACM Transactions on Mathematical Software (TOMS), 12(2):127--148, 1986.
[35]
Weizhuang Liu, Bo Yu, Yiming Gan, Qiang Liu, Jie Tang, Shaoshan Liu, and Yuhao Zhu. Archytas: A framework for synthesizing and dynamically optimizing accelerators for robotic localization. In MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture, page 479--493, 2021.
[36]
Daniel McGann, John G. Rogers, and Michael Kaess. Robust incremental smoothing and mapping (risam). In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 4157--4163, 2023.
[37]
Daniel Medeiros, Eduardo Cordeiro, Daniel Mendes, Maurício Sousa, Alberto Raposo, Alfredo Ferreira, and Joaquim Jorge. Effects of speed and transitions on target-based travel techniques. In Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology, VRST '16, page 327--328, New York, NY, USA, 2016. Association for Computing Machinery.
[38]
Nvidia. cuSOLVER. Accessed: 2023-12-01.
[39]
Nvidia. cuSPARSE. Accessed: 2023-12-01.
[40]
Nvidia. Jetson Nano. Accessed: 2023-12-01.
[41]
Nvidia. Nvidia Maxwell Architecture. Accessed: 2023-12-01.
[42]
NVIDIA-ISAAC-ROS. Isaac ros visual slam.
[43]
Daniele Palossi, Antonio Loquercio, Francesco Conti, Eric Flamand, Davide Scaramuzza, and Luca Benini. A 64-mw dnn-based visual navigation engine for autonomous nano-drones. IEEE Internet of Things Journal, pages 8357--8371, 2019.
[44]
Raspberry Pi Foundation. Raspberry Pi 4 Model B Specifications. Accessed: 2023-12-01.
[45]
David M Rosen, Michael Kaess, and John J Leonard. Rise: An incremental trust-region method for robust online sparse least-squares estimation. IEEE Transactions on Robotics, 30(5):1091--1108, 2014.
[46]
Antoni Rosinol, Marcus Abate, Yun Chang, and Luca Carlone. Kimera: an open-source library for real-time metric-semantic localization and mapping. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 1689--1696. IEEE, 2020.
[47]
Paul-Edouard Sarlin, Mihai Dusmanu, Johannes L. Schönberger, Pablo Speciale, Lukas Gruber, Viktor Larsson, Ondrej Miksik, and Marc Pollefeys. LaMAR: Benchmarking Localization and Mapping for Augmented Reality. In ECCV, 2022.
[48]
Xuesong Shi, Dongjiang Li, Pengpeng Zhao, Qinbin Tian, Yuxin Tian, Qiwei Long, Chunhao Zhu, Jingwei Song, Fei Qiao, Le Song, Yangquan Guo, Zhigang Wang, Yimin Zhang, Baoxing Qin, Wei Yang, Fangshi Wang, Rosa H. M. Chan, and Qi She. Are we ready for service robots? the openloris-scene datasets for lifelong slam. 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 3139--3145, 2019.
[49]
Amr Suleiman, Zhengdong Zhang, Luca Carlone, Sertac Karaman, and Vivienne Sze. Navion:Afully integrated energy-efficient visual-inertial odometry accelerator for autonomous navigation of nano drones. In 2018 IEEE Symposium on VLSI Circuits, pages 133--134, 2018.
[50]
Chen Wang, Dasong Gao, Kuan Xu, Junyi Geng, Yaoyu Hu, Yuheng Qiu, Bowen Li, Fan Yang, Brady Moon, Abhinav Pandey, Aryan, Jiahe Xu, Tianhao Wu, Haonan He, Daning Huang, Zhongqiang Ren, Shibo Zhao, Taimeng Fu, Pranay Reddy, Xiao Lin, Wenshan Wang, Jingnan Shi, Rajat Talak, Kun Cao, Yi Du, HanWang, Huai Yu, ShanzhaoWang, Siyu Chen, Ananth Kashyap, Rohan Bandaru, Karthik Dantu, Jiajun Wu, Lihua Xie, Luca Carlone, Marco Hutter, and Sebastian Scherer. PyPose: A library for robot learning with physics-based optimization. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[51]
Jian Weng, Sihao Liu, Zhengrong Wang, Vidushi Dadu, and Tony Nowatzki. A hybrid systolic-dataflow architecture for inductive matrix algorithms. In 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA), pages 703--716, 2020.
[52]
Zhengdong Zhang, Amr Suleiman, Luca Carlone, Vivienne Sze, and Sertac Karaman. Visual-inertial odometry on chip: An algorithm-and-hardware co-design approach. In Robotics: Science and Systems, 2017.
[53]
Zhengyou Zhang and Ying Shan. Incremental motion estimation through local bundle adjustment. 2001.
[54]
Jerry Zhao, Ben Korpan, Abraham Gonzalez, and Krste Asanovic. Sonicboom: The 3rd generation berkeley out-of-order machine. May 2020.
Index Terms
- SuperNoVA: Algorithm-Hardware Co-Design for Resource-Aware SLAM
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Published In
February 2025
1177 pages
ISBN:9798400706981
DOI:10.1145/3669940
- General Chairs:
- Lieven Eeckhout,
- Georgios Smaragdakis,
- Katai Liang,
- Program Chairs:
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- Martha Kim,
- Christopher J. Rossbach
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Published: 06 February 2025
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