Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
The emerging mobile robot industry has spurred a flurry of interest in solving the simultaneous localization and mapping (SLAM) problem. However, existing SLAM platforms have difficulty in meeting the real-time and low-power requirements imposed by mobile systems. Though specialized hardware is promising with regard to achieving high performance and lowering the power, designing an efficient accelerator for SLAM is severely hindered by a wide variety of SLAM algorithms. Based on our detailed analysis of representative SLAM algorithms, we observe that SLAM algorithms advance two challenges for designing efficient hardware accelerators: the large number of computational primitives and irregular control flows. To address these two challenges, we propose a hardware accelerator that features composable computation units classified as the matrix, vector, scalar, and control units. In addition, we design a hierarchical instruction set for coping with a broad range of SLAM algorithms with irregular control flows. Experimental results show that, compared against an Intel x86 processor, on average, our accelerator with the area of 7.41 mm2 achieves 10.52x and 112.62x better performance and energy savings, respectively, across different datasets. Compared against a more energy-efficient ARM Cortex processor, our accelerator still achieves 33.03x and 62.64x better performance and energy savings, respectively.
Durrant-Whyte H, Bailey T. Simultaneous localization and mapping: Part I. IEEE Robotics & Automation Magazine, 2006, 13(2): 99–110. DOI: 10.1109/MRA.2006.1638 022.
Guivant J E, Nebot E M. Optimization of the simultaneous localization and map-building algorithm for real-time implementation. IEEE Trans. Robotics and Automation, 2001, 17(3): 242–257. DOI: 10.1109/70.938382.
Wu Y K, Luo L, Yin S J, Yu M Q, Qiao F, Huang H Z, Shi X S, Wei Q, Liu X J. An FPGA based energy efficient DS-SLAM accelerator for mobile robots in dynamic environment. Applied Sciences, 2021, 11(4): 1–15. DOI: 10.3390/app11041828.
Bouhoun S, Sadoun R, Adnane M. OpenCL implementation of a SLAM system on an SoC-FPGA. Journal of Systems Architecture, 2020, 111: 101825. DOI: 10.1016/j.sysarc.2020.101825.
Nguyen D D, El Ouardi A, Rodríguez S, Bouaziz S. FPGA implementation of HOOFR bucketing extractor-based real-time embedded SLAM applications. Journal of Real-Time Image Processing, 2021, 18(3): 525–538. DOI: 10.1007/s11554-020-00986-9.
Czarnowski J, Laidlow T, Clark R, Davison A J. DeepFactors: Real-time probabilistic dense monocular SLAM. IEEE Robotics and Automation Letters, 2020, 5(2): 721–728. DOI: 10.1109/LRA.2020.2965415.
Li Y Y, Brasch N, Wang Y D, Navab N, Tombari F. Structure-SLAM: Low-drift monocular SLAM in indoor environments. IEEE Robotics and Automation Letters, 2020, 5(4): 6583–6590. DOI: 10.1109/LRA.2020.3015456.
Gomez-Ojeda R, Moreno F A, Zuniga-Noël D, Scaramuzza D, Gonzalez-Jimenez J. PL-SLAM: A stereo SLAM system through the combination of points and line segments. IEEE Trans. Robotics, 2019, 35(3): 734–746. DOI: 10.1109/TRO.2019.2899783.
Li X, Li Y Y, Örnek E P, Lin J L, Tombari F. Co-Planar parametrization for Stereo-SLAM and visual-inertial odometry. IEEE Robotics and Automation Letters, 2020, 5(4): 6972–6979. DOI: 10.1109/LRA.2020.3027230.
Endres F, Hess J, Sturm J, Cremers D, Burgard W. 3-D mapping with an RGB-D camera. IEEE Trans. Robotics, 2014, 30(1): 177–187. DOI: 10.1109/TRO.2013.2279412.
Kala S, Jose B R, Mathew J, Nalesh S. High-performance CNN accelerator on FPGA using unified winograd-GEMM architecture. IEEE Trans. Very Large Scale Integration (VLSI) Systems, 2019, 27(12): 2816–2828. DOI: 10.1109/TVLSI.2019.2941250.
Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91–110. DOI: 10.1023/B:VISI.0000029664.9961 5.94.
Strasdat H, Montiel J M M, Davison A J. Visual SLAM: Why filter? Image and Vision Computing, 2012, 30(2): 65–77. DOI: 10.1016/j.imavis.2012.02.009.
Arulampalam M S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Processing, 2002, 50(2): 174–188. DOI: 10.1109/78.978374.
Grisetti G, Stachniss C, Burgard W. Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Robotics, 2007, 23(1): 34–46. DOI: 10.1109/TRO.2006.889486.
Bailey T, Durrant-Whyte H. Simultaneous localization and mapping (SLAM): Part II. IEEE Robotics & Automation Magazine, 2006, 13(3): 108–117. DOI: 10.1109/MRA.2006.1678144.
Lu F, Milios E. Globally consistent range scan alignment for environment mapping. Autonomous Robots, 1997, 4(4): 333–349. DOI: 10.1023/A:1008854305733.
Grisetti G, Kummerle R, Stachniss C, Burgard W. A tutorial on graph-based SLAM. IEEE Intelligent Transportation Systems Magazine, 2010, 2(4): 31–43. DOI: 10.1109/MITS.2010.939925.
Mur-Artal R, Montiel J M M, Tardós J D. ORB-SLAM: A versatile and accurate monocular SLAM system. IEEE Trans. Robotics, 2015, 31(5): 1147–1163. DOI: 10.1109/TRO.2015.2463671.
Fischler M A, Bolles R C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981, 24(6): 381–395. DOI: 10.1145/358669.358692.
Stillmaker A, Baas B. Scaling equations for the accurate prediction of CMOS device performance from 180 nm to 7 nm. Integration, 2017, 58: 74–81. DOI: 10.1016/j.vlsi.2017.02.002.
Hong S, Kim J. Three-dimensional visual mapping of underwater ship hull surface using piecewise-planar SLAM. International Journal of Control, Automation and Systems, 2020, 18(3): 564–574. DOI: 10.1007/s12555-019-0646-8.
Wu L Y, Wan W G, Yu X Q, Ye C K, Muzahid A A M. A novel augmented reality framework based on monocular semi-dense simultaneous localization and mapping. Computer Animation and Virtual Worlds, 2020, 31(3): e1922. DOI: 10.1002/cav.1922.
Wen S H, Zhao Y F, Yuan X, Wang Z T, Zhang D, Manfredi L. Path planning for active SLAM based on deep reinforcement learning under unknown environments. Intelligent Service Robotics, 2020, 13(2): 263–272. DOI: 10.1007/s11370-019-00310-w.
Lee K Y, Byun K J. A hardware design of optimized ORB algorithm with reduced hardware cost. Advanced Science and Technology Letters, 2013, 43(3): 58–62. DOI: 10.14257/ASTL.2013.43.11.
Na E S, Jeong Y J. FPGA implementation of SURF-based feature extraction and descriptor generation. Journal of Korea Multimedia Society, 2013, 16(4): 483–492. DOI: 10.9717/KMMS.2013.16.4.483.
Jiang J, Li X Y, Zhang G J. SIFT hardware implementation for real-time image feature extraction. IEEE Trans. Circuits and Systems for Video Technology, 2014, 24(7): 1209–1220. DOI: 10.1109/TCSVT.2014.2302535.
Zhong S, Wang J H, Yan L X, Kang L, Cao Z G. A real-time embedded architecture for SIFT. Journal of Systems Architecture, 2013, 59(1): 16–29. DOI: 10.1016/j.sysarc.2012.09.002.
Huang F C, Huang S Y, Ker J W, Chen Y C. High-performance SIFT hardware accelerator for real-time image feature extraction. IEEE Trans. Circuits and Systems for Video Technology, 2012, 22(3): 340–351. DOI: 10.1109/TCSVT.2011.2162760.