Sonar was one of the first sensors with which mobile robots built maps, and in-air sonar keeps working where cameras and lidar fail, in darkness, smoke, dust and next to glass. Classical sonar SLAM, however, relied on geometric features or occupancy grids built from sparse range readings, and both struggle with the specular reflections that dominate indoor sonar. In this letter, we introduce RTISSLAM, a novel sonar-only SLAM system for an embedded 3D imaging sonar, which combines the robust pose graph of BatSLAM 2.0 as back-end with an occupancy grid as map. Energyscapes from delay-multiply-and-sum beamforming with a range-wise CFAR serve as place descriptors for sequence-verified loop closures, acoustic odometry calibrates the wheel odometry online, recognized revisits are registered metrically, and the grid is stored per keyframe and anchored to the pose-graph nodes, so that it follows every loop closure. In simulated worlds with specular walls, RTISSLAM reduces the trajectory error of the wheel odometry from \SI{8.2}{\meter} to \SI{0.31}{\meter} in an office, while lidar-style grid SLAM on the same sonar data fails.
RUSSO: Robust Underwater SLAM with Sonar Optimization against Visual Degradation
School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）机械工程与自动化学院）；School of Electrical and Information Engineering, Tianjin University（天津大学电气自动化与信息工程学院）
An essential task for a multi-robot system is generating a common understanding of the environment and relative poses between robots. Cooperative tasks can be executed only when a vehicle has knowledge of its own state and the states of the team members. However, this has primarily been achieved with direct rendezvous between underwater robots, via inter-robot ranging. We propose a novel distributed multi-robot simultaneous localization and mapping (SLAM) framework for underwater robots using imaging sonar-based perception. By passing only scene descriptors between robots, we do not need to pa…
Stevens Institute of Technology（斯蒂文斯理工学院）；Exyn Technologies（埃克森科技公司）
Simultaneous localization and mapping (SLAM) is a critical capability for any autonomous underwater vehicle (AUV). However, robust, accurate state estimation is still a work in progress when using low-cost sensors. We propose enhancing a typical low-cost sensor package using widely available and often free prior information; overhead imagery. Given an AUV's sonar image and a partially overlapping, globally-referenced overhead image, we propose using a convolutional neural network (CNN) to generate a synthetic overhead image predicting the above-surface appearance of the sonar image contents. W…
University of South Carolina（南卡罗来纳大学）；Dartmouth College（达特茅斯学院）
This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain. Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization -- one of the main problems affecting other packages in underwater domain -- by providing the following main contributions: a robust initialization method to refi…
VIMS: A Visual-Inertial-Magnetic-Sonar SLAM System in Underwater Environments
Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province, Zhejiang University（浙江省海洋观测-成像测试床重点实验室，浙江大学）；Interdisciplinary Student Training Platform for Marine areas, Zhejiang University（海洋领域交叉人才培养平台，浙江大学）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（电子与计算机工程系，香港科技大学）；The Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences（深海科学与工程研究院，中国科学院）
Matching sonar images with high accuracy has been a problem for a long time, as sonar images are inherently hard to model due to reflections, noise and viewpoint dependence. Autonomous Underwater Vehicles require good sonar image matching capabilities for tasks such as tracking, simultaneous localization and mapping (SLAM) and some cases of object detection/recognition. We propose the use of Convolutional Neural Networks (CNN) to learn a matching function that can be trained from labeled sonar data, after pre-processing to generate matching and non-matching pairs. In a dataset of 39K training …
Underwater Dense Mapping with the First Compact 3D Sonar
Mechanical Engineering Department, University of Delaware（德克萨斯大学机械工程系）；Dartmouth College（达特茅斯学院）；Aquaculture Robotics and Automation Group, SINTEF Ocean（SINTEF海洋研究所水产机器人与自动化小组）
Massachusetts Institute of Technology (MIT)（麻省理工学院）；University of Massachusetts Amherst (UMass Amherst)（马萨诸塞大学阿默斯特分校）
Modern robotic systems sense the environment geometrically, through sensors like cameras, lidar, and sonar, as well as semantically, often through visual models learned from data, such as object detectors. We aim to develop robots that can use all of these sources of information for reliable navigation, but each is corrupted by noise. Rather than assume that object detection will eventually achieve near perfect performance across the lifetime of a robot, in this work we represent and cope with the semantic and geometric uncertainty inherent in methods like object detection. Specifically, we mo…
LiDAR-BIND-T: Improved and Temporally Consistent Sensor Modality Translation and Fusion for Robotic Applications
IDLab - Faculty of Applied Engineering, University of Antwerp - imec（IDLab - 应用工程学院，安特卫普大学 - imec）；Cosys-Lab - Faculty of Applied Engineering, University of Antwerp（Cosys-Lab - 应用工程学院，安特卫普大学）；Flanders Make Strategic Research Centre（弗拉芒制造战略研究中心）
本文提出 LiDAR-BIND-T，通过时间嵌入相似度、运动对齐损失和窗口化时间融合增强异构传感器到 LiDAR 的转换，并以新指标验证其提升 SLAM 稳定性与精度。
