arXiv 2412.173432024-12-23cs.RO

端到端生成式时空超声里程计与建图框架

End-to-end Generative Spatial-Temporal Ultrasonic Odometry and Mapping Framework

University of Nottingham Ningbo China（宁波诺丁汉大学）；School of Computer Science, University of Nottingham Ningbo China（宁波诺丁汉大学计算机学院）

Fuhua Jia, Xiaoying Yang, Mengshen Yang, Yang Li, Hang Xu, Adam Rushworth, Salman Ijaz, Heng Yu, Tianxiang Cui

备注：: 5 pages, 4 figures and 1 table

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AI总结：

针对低能见度环境下的SLAM难题，提出端到端生成式超声SLAM框架，利用重叠视场传感器阵列与滑动窗口机制进行时空编码，生成点云与位姿变换，克服超声传感器固有缺陷并验证了其可行性与稳健性。

摘要：

在充满烟雾、灰尘和透明物体等低能见度条件下执行同步定位与建图（SLAM）一直是一项具有挑战性的任务。相机和激光雷达（LiDAR）等传感器在这些条件下受到显著限制，而超声波传感器则提供了一种更稳健的替代方案。然而，超声波传感器的低角分辨率、慢更新频率和有限的检测精度构成了SLAM的障碍。在这项工作中，我们提出了一种新颖的端到端生成式超声波SLAM框架。该框架采用具有重叠视场的传感器阵列，利用超声波传感器固有的低角分辨率，结合机器人运动隐式编码空间特征。连续时间帧数据通过滑动窗口机制进行处理，以捕获时间特征。时空编码的传感器数据通过多个模块，生成密集扫描点云和机器人位姿变换，用于地图构建和里程计。这项工作的主要贡献包括一个对周围环境进行时空编码的新型超声波传感器阵列，以及一个克服超声波传感器固有缺陷的端到端生成式SLAM框架。多项真实世界实验证明了所提框架的可行性与稳健性。

英文摘要：

Performing simultaneous localization and mapping (SLAM) in low-visibility conditions, such as environments filled with smoke, dust and transparent objets, has long been a challenging task. Sensors like cameras and Light Detection and Ranging (LiDAR) are significantly limited under these conditions, whereas ultrasonic sensors offer a more robust alternative. However, the low angular resolution, slow update frequency, and limited detection accuracy of ultrasonic sensors present barriers for SLAM. In this work, we propose a novel end-to-end generative ultrasonic SLAM framework. This framework employs a sensor array with overlapping fields of view, leveraging the inherently low angular resolution of ultrasonic sensors to implicitly encode spatial features in conjunction with the robot's motion. Consecutive time frame data is processed through a sliding window mechanism to capture temporal features. The spatiotemporally encoded sensor data is passed through multiple modules to generate dense scan point clouds and robot pose transformations for map construction and odometry. The main contributions of this work include a novel ultrasonic sensor array that spatiotemporally encodes the surrounding environment, and an end-to-end generative SLAM framework that overcomes the inherent defects of ultrasonic sensors. Several real-world experiments demonstrate the feasibility and robustness of the proposed framework.

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