Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar
Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar
复制标题
使用机械扫描成像声纳的基于姿态的 SLAM，具有概率扫描匹配算法
DOI：
10.1109/oceanse.2009.5278219
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Y. Pétillot
中科院分区：
文献类型：
--
作者：
A. Mallios;P. Ridao;E. Hernández;D. Ribas;F. Maurelli;Y. Pétillot
本文提出了一种基于姿态的算法来解决自主水下航行器（AUV）在未知且可能非结构化环境中航行的完整SLAM问题。该技术结合概率扫描匹配与距离扫描收集从机械扫描成像声纳（MSIS）和机器人航位推算位移估计从多普勒速度日志（DVL）和运动参考单元（MRU）。该方法采用两个扩展卡尔曼滤波器（EKF）。第一，估计机器人在抓取扫描时走过的局部路径及其不确定性，并提供位置估计，用于校正车辆运动在声学图像中产生的失真。第二个是增强状态EKF，估计并保持注册的扫描姿势。来自传感器的原始数据被处理并在线融合。不考虑先验结构信息或初始姿态。该算法已被测试的AUV引导沿着600米的路径内的码头环境，显示所提出的方法的可行性。
This paper proposes a pose-based algorithm to solve the full SLAM problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler Velocity Log (DVL) and a Motion Reference Unit (MRU). The proposed method utilizes two Extended Kalman Filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600m path within a marina environment, showing the viability of the proposed approach.