invited-talk

Factor Graph Accelerator for LiDAR-Inertial Odometry (Invited Paper)

Authors:

Yuhao ZhuAuthors Info & Claims

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

Article No.: 103, Pages 1 - 7

https://doi.org/10.1145/3508352.3561112

Published: 22 December 2022 Publication History

Abstract

Factor graph is a graph representing the factorization of a probability distribution function, and has been utilized in many autonomous machine computing tasks, such as localization, tracking, planning and control etc. We are developing an architecture with the goal of using factor graph as a common abstraction for most, if not, all autonomous machine computing tasks. If successful, the architecture would provide a very simple interface of mapping autonomous machine functions to the underlying compute hardware. As a first step of such an attempt, this paper presents our most recent work of developing a factor graph accelerator for LiDAR-Inertial Odometry (LIO), an essential task in many autonomous machines, such as autonomous vehicles and mobile robots. By modeling LIO as a factor graph, the proposed accelerator not only supports multi-sensor fusion such as LiDAR, inertial measurement unit (IMU), GPS, etc., but solves the global optimization problem of robot navigation in batch or incremental modes. Our evaluation demonstrates that the proposed design significantly improves the real-time performance and energy efficiency of autonomous machine navigation systems. The initial success suggests the potential of generalizing the factor graph architecture as a common abstraction for autonomous machine computing, including tracking, planning, and control etc.

References

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Cited By

Huang LGong YSui YZang XYuan B(2024)MOPED: Efficient Motion Planning Engine with Flexible Dimension Support2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00043(483-497)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00043

Index Terms

Factor Graph Accelerator for LiDAR-Inertial Odometry (Invited Paper)
1. Computer systems organization
2. Hardware
  1. Integrated circuits
    1. Reconfigurable logic and FPGAs
      1. Hardware accelerators

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

October 2022

1467 pages

ISBN:9781450392174

DOI:10.1145/3508352

Conference Chair:
Tulika Mitra
National University of Singapore
,
Program Chairs:
Evangeline Young
The Chinese University of Hong Kong
,
Jinjun Xiong
University at Buffalo (UB)

Copyright © 2022 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 22 December 2022

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ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design

October 30 - November 3, 2022

California, San Diego

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Cited By

Huang LGong YSui YZang XYuan B(2024)MOPED: Efficient Motion Planning Engine with Flexible Dimension Support2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00043(483-497)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00043

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