ER-Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS IET Cybersystems and Robotics Pub Date : 2024-05-23 DOI:10.1049/csy2.12116
Changjian Jiang, Zeyu Wan, Ruilan Gao, Yu Zhang
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Abstract

The colour-enhanced point cloud map is increasingly being employed in fields such as robotics, 3D reconstruction and virtual reality. The authors propose ER-Mapping (Extrinsic Robust coloured Mapping system using residual evaluation and selection). ER-Mapping consists of two components: the simultaneous localisation and mapping (SLAM) subsystem and the colouring subsystem. The SLAM subsystem reconstructs the geometric structure, where it employs a dynamic threshold-based residual selection in LiDAR-inertial odometry to improve mapping accuracy. On the other hand, the colouring subsystem focuses on recovering texture information from input images and innovatively utilises 3D–2D feature selection and optimisation methods, eliminating the need for strict hardware time synchronisation and highly accurate extrinsic parameters. Experiments were conducted in both indoor and outdoor environments. The results demonstrate that our system can enhance accuracy, reduce computational costs and achieve extrinsic robustness.

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ER-Mapping:使用残差评估和选择的外在稳健彩色绘图系统
色彩增强点云图在机器人、三维重建和虚拟现实等领域的应用越来越广泛。作者提出了 ER-Mapping(使用残差评估和选择的外在鲁棒彩色绘图系统)。ER-Mapping 由两个部分组成:同步定位与绘图(SLAM)子系统和着色子系统。同步定位与绘图子系统重建几何结构,在激光雷达-惯性里程测量中采用基于阈值的动态残差选择,以提高绘图精度。另一方面,着色子系统侧重于从输入图像中恢复纹理信息,并创新性地利用三维-二维特征选择和优化方法,无需严格的硬件时间同步和高精度的外在参数。实验在室内和室外环境中进行。结果表明,我们的系统可以提高精确度、降低计算成本并实现外在鲁棒性。
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来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
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