融合事件的点线特征法视觉惯性里程计

刘毓敏; 蔡志浩; 孙家岭; 赵江; 王英勋

doi:10.12382/bgxb.2023.1221

您当前的位置：

首页 >

文章列表页 >

融合事件的点线特征法视觉惯性里程计

更新时间：2025-08-18

- 融合事件的点线特征法视觉惯性里程计
- Event-combined Visual-inertial Odometry Using Point and Line Features
- 兵工学报 2024年45卷第11期页码：3926-3937
- 作者机构：
  
  1. 北京航空航天大学自动化科学与电气工程学院, 北京 100191
  2. 北京航空航天大学无人系统研究院, 北京 100191
- 作者简介：
  
  *邮箱: czh@buaa.edu.cn
- 基金信息：
  
  飞行器控制一体化国家重点实验室基金项目(11300LB2022103007)
- DOI：10.12382/bgxb.2023.1221
  中图分类号：
- 收稿：2023-12-29，
  
  网络出版：2024-11-26，
  
  纸质出版：2024-11-30
- 稿件说明：
移动端阅览
刘毓敏, 蔡志浩, 孙家岭, 等. 融合事件的点线特征法视觉惯性里程计[J]. 兵工学报, 2024,45(11):3926-3937.

Yumin LIU, Zhihao CAI, Jialing SUN, et al. Event-combined Visual-inertial Odometry Using Point and Line Features[J]. Acta Armamentarii, 2024, 45(11): 3926-3937.
刘毓敏, 蔡志浩, 孙家岭, 等. 融合事件的点线特征法视觉惯性里程计[J]. 兵工学报, 2024,45(11):3926-3937. DOI： 10.12382/bgxb.2023.1221.

Yumin LIU, Zhihao CAI, Jialing SUN, et al. Event-combined Visual-inertial Odometry Using Point and Line Features[J]. Acta Armamentarii, 2024, 45(11): 3926-3937. DOI： 10.12382/bgxb.2023.1221.

摘要

视觉惯性里程计是机器人实现自主定位的关键技术

事件相机作为一种异步视觉传感器

与传统相机具有互补的特点。针对低光照、光照大幅度变化和高速运动场景

对事件相机的输出和传统图像进行融合

并结合惯性测量单元进行实时点线特征法视觉惯性里程计研究。提出一种从事件流生成事件图像的算法

设计融合事件的点线特征检测方法;基于视觉-惯性紧耦合的思想

设计后端滑动窗口优化算法;进行数据集试验验证和无人机飞行试验验证。在数据集上的试验结果表明:与仅使用传统图像的点线特征法视觉惯性里程计相比

在高速运动的场景下

定位误差平均减少了22%以上;在低光照、光照大幅度变化的场景下

定位误差平均减少了59%以上。

Abstract

Visual-inertial odometry is a key technology for robots to achieve autonomous localization. As an asynchronous vision sensor

the event cameras have complementary to the traditional cameras. For the scene of low light condition

high dynamic range and high-speed motion

the output of event camera and the traditional image are fused.A real-time visual inertial odometry using point and line features is proposed combined with the inertial measurement unit (IMU). An algorithm for generating an event image from event stream is proposed

a point-line feature detection method combined with events is designed

anda back-end sliding window optimization algorithm is designed based on the idea of visual-inertial tight-coupling. The dataset test and UAV flight test are conducted. The test results on the dataset show that

compared with the visual-inertial odometry using point and line features only on the traditional image

the proposed odometry can reduce the positioning error by more than 22% on average in the scene of high-speed motion

and it can reducethe positioning error by more than 59% on average in the scene of low light condition and high dynamic range.

关键词

Keywords

references

张福斌 , 张炳烁 , 杨玉帅 . 基于单目/IMU/里程计融合的SLAM算法 [J ] . 兵工学报 , 2022 , 43 ( 11 ): 2810 - 2818 .

ZHANG F B , ZHANG B S , YANG Y S . SLAM algorithm basedonmonocular/IMU/odometer fusion [J ] . Acta Armamentarii , 2022 , 43 ( 11 ): 2810 - 2818 . (in Chinese)

GALLEGO G , DELBRUCK T , ORCHARD G , et al. Event-based vision: a survey [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020 , 44 ( 1 ): 154 - 180 .

邹浩东 , 昌敏 , 郭金库 , 等 . 基于事件的视觉SLAM综述 [J ] . 无人系统技术 , 2023 , 6 ( 3 ): 59 - 80 .

ZOU H D , CHANG M , GUO J K , et al. Review of event-based visual SLAM [J ] . Unmanned Systems Technology , 2023 , 6 ( 3 ): 59 - 80 . (in Chinese)

FALANGA D , KLEBER K , SCARAMUZZA D . Dynamic obstacle avoidance for quadrotors with event cameras [J ] . Science Robotics , 2020 , 5 ( 40 ): eaaz9712.

ZHOU Y , GALLEGO G , SHEN S J . Event-based stereo visual odometry [J ] . IEEE Transactions on Robotics , 2021 , 37 ( 5 ): 1433 - 1450 .

HIDALGO J , GALLEGO G , SCARAMUZZA D . Event-aided direct sparse odometry [C ] // Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans, LA,US:IEEE , 2022 : 5781 - 5790 .

GAO L , SU H , GEHRIG D , et al. A 5-point minimal solver for event camera relative motion estimation [C ] // Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Paris,France:IEEE , 2023 : 8049 - 8059 .

ZUBIC N , GEHRIG D , GEHRIG M , et al. From chaos comes order: ordering event representations for object recognition and detection [C ] // Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Paris,France:IEEE , 2023 : 12846 - 12856 .

SUN Z , MESSIKOMMER N , GEHRIG D , et al. ESS: learning event-based semantic segmentation from still images [C ] // Proceedings of 17th European Conference on Computer Vision. Tel Aviv , Israel : Springer , 2022 : 341 - 357 .

MESSIKOMMER N , FANG C , GEHRIG M , et al. Data-driven feature tracking for event cameras [C ] // Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver, BC,Canada:IEEE , 2023 : 5642 - 5651 .

CHEN P Y , GUAN W P , LU P . ESVIO: event-based stereo visual inertial odometry [J ] . IEEE Robotics and Automation Letters , 2023 , 8 ( 6 ): 3661 - 3668 .

MAHLKNECHT F , GEHRIG D , NASH J , et al. Exploring event camera-based odometry for planetary robots [J ] . IEEE Robotics and Automation Letters , 2022 , 7 ( 4 ): 8651 - 8658 .

FU Q , WANG J L , YU H S , et al. PL-VINS: real-time monocular visual-inertial SLAM with point and line features:arXiv:2009.07462 [R/OL ] . Ithaca, NY , US : Cornell University , 2020 (2020-09-16). https://arxiv.org/abs/2009.07462 https://arxiv.org/abs/2009.07462 https://arxiv.org/abs/2009.07462.

LIM H , JEON J , MYUNG H . UV-SLAM: unconstrained line-based SLAM using vanishing points for structural mapping [J ] . IEEE Robotics and Automation Letters , 2022 , 7 ( 2 ): 1518 - 1525 .

LI H A , ZHAO J , BAZIN J C , et al. Quasi-globally optimal and near/true real-time vanishing point estimation in Manhattan world [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020 , 44 ( 3 ): 1503 - 1518 .

XU B , WANG P , HE Y J , et al. Leveraging structural information to improve point line visual-inertial odometry [J ] . IEEE Robotics and Automation Letters , 2022 , 7 ( 2 ): 3483 - 3490 .

LEE J , PARK S Y . PLF-VINS: real-time monocular visual-inertial SLAM with point-line fusion and parallel-line fusion [J ] . IEEE Robotics and Automation Letters , 2021 , 6 ( 4 ): 7033 - 7040 .

QIN T , LI P L , SHEN S J . VINS-Mono: a robust and versatile monocular visual-inertial state estimator [J ] . IEEE Transactions on Robotics , 2018 , 34 ( 4 ): 1004 - 1020 .

FURGALE P , REHDER J , SIEGWART R . Unified temporal and spatial calibration for multi-sensor systems [C ] // Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo, Japan:IEEE , 2013 : 1280 - 1286 .

MUEGGLER E , REBECQ H , GALLEGO G , et al. The event-camera dataset and simulator: event-based data for pose estimation, visual odometry, and SLAM [J ] . The International Journal of Robotics Research , 2017 , 36 ( 2 ): 142 - 149 .

VIDAL A , REBECQ H , HORSTSCHAEFER T . Ultimate SLAM? combining events, images, and IMU for robust visual SLAM in HDR and high-speed scenarios [J ] . IEEE Robotics and Automation Letters , 2018 , 3 ( 2 ): 994 - 1001 .

REBECQ H , HORSTSCHAEFER T , SCARAMUZZA D . Real-time visual-inertial odometry for event cameras using keyframe-based nonlinear optimization [C ] // Proceedings of the 28th British Machine Vision Conference. London , UK : BMVA Press , 2017 .

WANG Y X , SHAO B , ZHANG C C , et al. REVIO: range-and event-based visual-inertial odometry for bio-inspired sensors [J ] . Biomimetics , 2022 , 7 ( 4 ): 169.

浏览量

592

下载量

CNKI被引量

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据