光流
稳健性(进化)
人工智能
计算机科学
计算机视觉
同时定位和映射
图形
特征(语言学)
计算复杂性理论
可视化
有界函数
算法
机器人
理论计算机科学
数学
移动机器人
图像(数学)
生物化学
语言学
基因
数学分析
哲学
化学
作者
Hongle Xie,Weidong Chen,Jingchuan Wang,Hesheng Wang
出处
期刊:International Conference on Robotics and Automation
日期:2020-05-01
被引量:5
标识
DOI:10.1109/icra40945.2020.9197278
摘要
Accurate, robust and real-time localization under constrained-resources is a critical problem to be solved. In this paper, we present a new sparse pose-graph visual-inertial SLAM (SPVIS). Unlike the existing methods that are costly to deal with a large number of redundant features and 3D map points, which are inefficient for improving positioning accuracy, we focus on the concise visual cues for high-precision pose estimating. We propose a novel hierarchical quadtree based optical flow tracking algorithm, it achieves high accuracy and robustness within very few concise features, which is only about one fifth features of the state-of-the-art visual-inertial SLAM algorithms. Benefiting from the efficient optical flow tracking, our sparse pose-graph optimization time cost achieves bounded complexity. By selecting and optimizing the informative features in sliding window and local VIO, the computational complexity is bounded, it achieves low time cost in long-term operation. We compare with the state-of-the-art VIO/VI-SLAM systems on the challenging public datasets by the embedded platform without GPUs, the results effectively verify that the proposed method has better real-time performance and localization accuracy.
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