PIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design

Zhang, Zhe; Liu, Shaoshan; Tsai, Grace; Hu, Hsiao Yun; Chu, Chen-Chi; Zheng, Feng

doi:10.1109/icra.2018.8460672

Cited by 42 publications

(25 citation statements)

References 22 publications

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“…LiDAR shows good performance with a range from several centimeters to 200 meters, and the accuracy of distance goes to centimeterlevel. LiDAR is widely used in object detection, distance estimation, edge detection, Simultaneously Localization and Mapping (SLAM) [38], [11], and High-Definition (HD) Map generation [39], [40], [41], [12]. However, in terms of the cost, LiDAR seems less competitive with other sensors.…”

Section: Key Technologiesmentioning

confidence: 99%

Edge Computing for Autonomous Driving: Opportunities and Challenges

Liu¹,

et al. 2019

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The recent proliferation of computing technologies, e.g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e.g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors. The key to the success of these autonomous systems is making a reliable decision in a real-time fashion. However, accidents and fatalities caused by early deployed autonomous vehicles arise from time to time. The real traffic environment is too complicated for the current autonomous driving computing systems to understand and handle. In this paper, we present the state-of-the-art computing systems for autonomous driving, including seven performance metrics and nine key technologies, followed by eleven challenges and opportunities to realize autonomous driving. We hope this paper will gain attention from both the computing and automotive communities and inspire more research in this direction.

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Section: Key Technologiesmentioning

confidence: 99%

Edge Computing for Autonomous Driving: Opportunities and Challenges

Liu¹,

et al. 2019

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“…In DragonFly pod, we use our proprietary SLAM system [7], [8] that utilizes a stereo camera for image generation at 60 FPS, with each frame having the size of 640 X 480 pixels. Meanwhile, the IMU device generates 200 Hz of IMU updates (three axes of angular velocity and three axes of acceleration).…”

Section: ) Localizationmentioning

confidence: 99%

LoPECS: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services

Tang

Liu²,

Liu

et al. 2020

IEEE Access

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To simultaneously enable multiple autonomous driving services on affordable embedded systems, we designed and implemented LoPECS, a Low-Power Edge Computing System for real-time autonomous robots and vehicles services. The contributions of this paper are threefold: first, we developed a Heterogeneity-Aware Runtime Layer to fully utilize vehicle's heterogeneous computing resources to fulfill the real-time requirement of autonomous driving applications; second, we developed a vehicle-edge Coordinator to dynamically offload vehicle tasks to edge cloudlet to further optimize user experience in the way of prolonged battery life; third, we successfully integrated these components into LoPECS system and implemented it on Nvidia Jetson TX1. To the best of our knowledge, this is the first complete edge computing system in a production autonomous vehicle. Our implementation on Nvidia Jetson demonstrated that it could successfully support multiple autonomous driving services with only 11 W of power consumption, and hence proves the effectiveness of the proposed LoPECS system. INDEX TERMS Edge computing, QoE (quality of experience), low power, autonomous driving.

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“…Fusion Type Application OKVIS [43][44][45] optimization-based monocular tightly coupled SR-ISWF [46] filtering-based monocular tightly coupled mobile phone [47] optimization-based monocular tightly coupled [48] optimization-based Stereo tightly coupled MAV [49] optimization-based rgb-d loosely coupled Mobile devices [50] filtering-based monocular tightly coupled ROVIO [51] filtering-based monocular tightly coupled UAV [52] optimization-based monocular tightly coupled autonomous vehicle [53] filtering-based stereo tightly coupled [54] optimization-based stereo tightly coupled [55] optimization-based monocular tightly coupled [56] optimization-based stereo tightly coupled [57] filtering-based monocular loosely coupled robot [58] optimization-based rgb-d loosely coupled [59] filtering-based stereo loosely coupled VIORB [60] optimization-based monocular tightly coupled MAV [61] optimization-based rgb-d tightly coupled [62] filtering-based monocular loosely coupled AR/VR [63] filtering-based Multi-camera tightly coupled MAV [64] filtering-based monocular tightly coupled UAV VINS-mono [16][17][18] optimization-based monocular tightly coupled MAV, AR [65] optimization-based monocular tightly coupled AR [66] optimization-based monocular tightly coupled [67] filtering-based monocular tightly coupled MAV VINet [68] end-to-end monocular / deep-learning [69] optimization-based event camera tightly coupled S-MSCKF [26] filtering-based stereo tightly coupled MAV [70] optimization-based monocular tightly coupled MAV [71] optimization-based stereomonocular tightly coupled PIRVS [72] filtering-based st...…”

Section: Year Paper Back-end Approach Camera Typementioning

confidence: 99%

A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

et al. 2018

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Visual-inertial simultaneous localization and mapping (VI-SLAM) is popular research topic in robotics. Because of its advantages in terms of robustness, VI-SLAM enjoys wide applications in the field of localization and mapping, including in mobile robotics, self-driving cars, unmanned aerial vehicles, and autonomous underwater vehicles. This study provides a comprehensive survey on VI-SLAM. Following a short introduction, this study is the first to review VI-SLAM techniques from filtering-based and optimization-based perspectives. It summarizes state-of-the-art studies over the last 10 years based on the back-end approach, camera type, and sensor fusion type. Key VI-SLAM technologies are also introduced such as feature extraction and tracking, core theory, and loop closure. The performance of representative VI-SLAM methods and famous VI-SLAM datasets are also surveyed. Finally, this study contributes to the comparison of filtering-based and optimization-based methods through experiments. A comparative study of VI-SLAM methods helps understand the differences in their operating principles. Optimization-based methods achieve excellent localization accuracy and lower memory utilization, while filtering-based methods have advantages in terms of computing resources. Furthermore, this study proposes future development trends and research directions for VI-SLAM. It provides a detailed survey of VI-SLAM techniques and can serve as a brief guide to newcomers in the field of SLAM and experienced researchers looking for possible directions for future work.

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PIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design

Cited by 42 publications

References 22 publications

Edge Computing for Autonomous Driving: Opportunities and Challenges

Edge Computing for Autonomous Driving: Opportunities and Challenges

LoPECS: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services

A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

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