A Survey of FPGA-Based Robotic Computing

Wan, Zishen; Bo, Yu; Li, Thomas Yuang; Tang, Jie; Zhu, Yuhao; Wang, Yu; Raychowdhury, Arijit; Liu, Shaoshan

doi:10.1109/mcas.2021.3071609

Cited by 56 publications

(37 citation statements)

References 158 publications

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“…Dense and semi-dense maps provide a more detailed representation of the environment, but this feature has consequences for resource usage. It has already been demonstrated that sparse maps present lower power consumption compared to semi-dense and dense ones-Wan et al [115]. Consequently, they may be more suitable for an embedded implementation, although they provide fewer details.…”

Section: Open Problems and Future Directionsmentioning

confidence: 99%

A Comprehensive Survey of Visual SLAM Algorithms

et al. 2022

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Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors’ pose estimation in an unknown environment. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. The literature presents different approaches and methods to implement visual-based SLAM systems. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. Furthermore, we propose six criteria that ease the SLAM algorithm’s analysis and consider both the software and hardware levels. In addition, we present some major issues and future directions on visual-SLAM field, and provide a general overview of some of the existing benchmark datasets. This work aims to be the first step for those initiating a SLAM project to have a good perspective of SLAM techniques’ main elements and characteristics.

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Section: Open Problems and Future Directionsmentioning

confidence: 99%

A Comprehensive Survey of Visual SLAM Algorithms

et al. 2022

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“…On the other hand, to achieve real-time performance, software approaches such as the compression-compiler co-design method that combines the compression of deep learning models and their compilation to optimize both the size and speed of deep learning models [7,8]. Hardware approaches such as hardware accelerators for perception modules [9,10] can be taken. Ultimately, more research is required to determine what combination of software and hardware approaches will be most effective in achieving real-time performance for the perception system.…”

Section: Challengementioning

confidence: 99%

Rise of the Autonomous Machines

Liu¹,

Gaudiot

2022

Computer

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After decades of uninterrupted progress and growth, information technology has so evolved that it can be said we are entering the age of autonomous machines, but there exist many roadblocks in the way of making this a reality. In this article, we make a preliminary attempt at recognizing and categorizing the technical and non-technical challenges of autonomous machines; for each of the ten areas we have identified, we review current status, roadblocks, and potential research directions. It is hoped that this will help the community define clear, effective, and more formal development goalposts for the future.

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“…FPGAs are attracting attention due to its reconfigurability and hardwareefficiency, and have been presented for robotic perception [2], [3], localization [4], [5], and planning [6], [7]. The partial reconfiguration technique takes this flexibility one step further, where part of FPGA resources can be reconfigured at runtime without compromising the other parts of applications [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Circuit and System Technologies for Energy-Efficient Edge Robotics: (Invited Paper)

Wan

Lele

Raychowdhury

2022

2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)

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As we march towards the age of ubiquitous intelligence, we note that AI and intelligence are progressively moving from the cloud to the edge. The success of Edge-AI is pivoted on innovative circuits and hardware that can enable inference and limited learning in resource-constrained edge autonomous systems. This paper introduces a series of ultra-low-power accelerator and system designs on enabling the intelligence in edge robotic platforms, including reinforcement learning neuromorphic control, swarm intelligence, and simultaneous mapping and localization. We put an emphasis on the impact of the mixedsignal circuit, neuro-inspired computing system, benchmarking and software infrastructure, as well as algorithm-hardware codesign to realize the most energy-efficient Edge-AI ASICs for the next-generation intelligent and autonomous systems.

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A Survey of FPGA-Based Robotic Computing

Cited by 56 publications

References 158 publications

A Comprehensive Survey of Visual SLAM Algorithms

A Comprehensive Survey of Visual SLAM Algorithms

Rise of the Autonomous Machines

Circuit and System Technologies for Energy-Efficient Edge Robotics: (Invited Paper)

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