Visual Simultaneous Localization and Mapping (v-SLAM) and navigation of multirotor Unmanned Aerial Vehicles (UAV) in an unknown environment have grown in popularity for both research and education. However, due to the complex hardware setup, safety precautions, and battery constraints, extensive physical testing can be expensive and time-consuming. As an alternative solution, simulation tools lower the barrier to carry out the algorithm testing and validation before field trials. In this letter, we customize the ROS-Gazebo-PX4 simulator in deep and provide an end-to-end simulation solution for the UAV v-SLAM and navigation study. A set of localization, mapping, and path planning kits were also integrated into the simulation platform. In our simulation, various aspects, including complex environments and onboard sensors, can simultaneously interact with our navigation framework to achieve specific surveillance missions. In this end-to-end simulation, we achieved click and fly level autonomy UAV navigation. The source code is open to the research community.
University of Zagreb, Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算学院）
This paper describes an application of the Cartographer graph SLAM stack as a pose sensor in a UAV feedback control loop, with certain application-specific changes in the SLAM stack such as smoothing of the optimized pose. Pose estimation is performed by fusing 3D LiDAR/IMU-based proprioception with GPS position measurements by means of pose graph optimisation. Moreover, partial environment maps built from the LiDAR data (submaps) within the Cartographer SLAM stack are marshalled into OctoMap, an Octree-based voxel map implementation. The OctoMap is further used for navigation tasks such as pa…
We introduce LIDAUS Localization of IoT Device via Anchor UAV SLAM), an infrastructure-free, multi-stage SLAM system that utilizes an Unmanned Aerial Vehicle (UAV) to accurately localize IoT devices in a 3D indoor space where GPS signals are unavailable or weak, e.g., manufacturing factories, disaster sites, or smart buildings. The lack of GPS signals and infrastructure support makes most of the existing indoor localization systems not practical when localizing a large number of wireless IoT devices. In addition, safety concerns, access restriction, and simply the huge amount of IoT devices ma…
Task-driven SLAM Benchmarking For Robot Navigation
School of Electrical and Computer Engineering, and Institute of Robotics and Intelligent Machines, Georgia Institute of Technology（电气与计算机工程学院，机器人与智能机器研究所，佐治亚理工学院）；School of Mechanical Engineering and the School of Electrical and Computer Engineering, Georgia Institute of Technology（机械工程学院，电气与计算机工程学院，佐治亚理工学院）
Aalto University（阿尔托大学）；University of Bristol（布里斯托大学）；University of the West of England（西英格兰大学）
RatSLAM is a rat hippocampus-inspired visual Simultaneous Localization and Mapping (SLAM) framework capable of generating semi-metric topological representations of indoor and outdoor environments. Whisker-RatSLAM is a 6D extension of the RatSLAM and primarily focuses on object recognition by generating point clouds of objects based on whisking information. This paper introduces a novel extension to both former works that is referred to as ViTa-SLAM that harnesses both vision and tactile information for performing SLAM. This not only allows the robot to perform natural interaction with the env…
3D reconstruction can be used as a platform to monitor the performance of activities on construction site, such as construction progress monitoring, structure inspection and post-disaster rescue. Comparing to other sensors, RGB image has the advantages of low-cost, texture rich and easy to implement that has been used as the primary method for 3D reconstruction in construction industry. However, the image-based 3D reconstruction always requires extended time to acquire and/or to process the image data, which limits its application on time critical projects. Recent progress in Visual Simultaneo…
Artificial Intelligence Research Institute, Federal Research Center for Computer Science and Control of Russian Academy of Sciences（俄罗斯科学院计算机科学与控制联邦研究中心人工智能研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）
Exploration is an important step in autonomous navigation of robotic systems. In this paper we introduce a series of enhancements for exploration algorithms in order to use them with vision-based simultaneous localization and mapping (vSLAM) methods. We evaluate developed approaches in photo-realistic simulator in two modes: with ground-truth depths and neural network reconstructed depth maps as vSLAM input. We evaluate standard metrics in order to estimate exploration coverage.
The Hong Kong University of Science and Technology（香港科技大学）
Visible light positioning (VLP) technology is a promising technique as it can provide high accuracy positioning based on the existing lighting infrastructure. However, existing approaches often require dense lighting distributions. Additionally, due to complicated indoor environments, it is still challenging to develop a robust VLP. In this work, we proposed loosely-coupled multi-sensor fusion method based on VLP and Simultaneous Localization and Mapping (SLAM), with light detection and ranging (LiDAR), odometry, and rolling shutter camera. Our method can provide accurate and robust robotics l…
AgriLiRa4D: A Multi-Sensor UAV Dataset for Robust SLAM in Challenging Agricultural Fields
School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；TopXGun Robotics（TopXGun机器人公司）；School of Computer Science, Hangzhou Dianzi University（杭州电子科技大学计算机科学学院）
Simultaneous localization and mapping (SLAM) remains challenging for a number of downstream applications, such as visual robot navigation, because of rapid turns, featureless walls, and poor camera quality. We introduce the Differentiable SLAM Network (SLAM-net) along with a navigation architecture to enable planar robot navigation in previously unseen indoor environments. SLAM-net encodes a particle filter based SLAM algorithm in a differentiable computation graph, and learns task-oriented neural network components by backpropagating through the SLAM algorithm. Because it can optimize all mod…
MINES ParisTech, PSL Research University（巴黎高等矿业学院，巴黎文理研究大学）
The Extended Kalman Filter (EKF) is both the historical algorithm for multi-sensor fusion and still state of the art in numerous industrial applications. However, it may prove inconsistent in the presence of unobservability under a group of transformations. In this paper we first build an alternative EKF based on an alternative nonlinear state error. This EKF is intimately related to the theory of the Invariant EKF (IEKF). Then, under a simple compatibility assumption between the error and the transformation group, we prove the linearized model of the alternative EKF automatically captures the…
Gaining spatial awareness of the Operating Room (OR) for surgical robotic systems is a key technology that can enable intelligent applications aiming at improved OR workflow. In this work, we present a method for semantic dense reconstruction of the OR scene using multiple RGB-D cameras attached and registered to the da Vinci Xi surgical system. We developed a novel SLAM approach for robot pose tracking in dynamic OR environments and dense reconstruction of the static OR table object. We validated our techniques in a mock OR by collecting data sequences with corresponding optical tracking traj…
Autonomous Systems Lab, ETH Zurich（苏黎世联邦理工学院自主系统实验室）
Visual robot navigation within large-scale, semi-structured environments deals with various challenges such as computation intensive path planning algorithms or insufficient knowledge about traversable spaces. Moreover, many state-of-the-art navigation approaches only operate locally instead of gaining a more conceptual understanding of the planning objective. This limits the complexity of tasks a robot can accomplish and makes it harder to deal with uncertainties that are present in the context of real-time robotics applications. In this work, we present Topomap, a framework which simplifies …
Universidad de Chile（智利大学）；Advanced Mining Technology Center（先进采矿技术中心）；Delft University of Technology（代尔夫特理工大学）
We propose a Visual-SLAM based localization and navigation system for service robots. Our system is built on top of the ORB-SLAM monocular system but extended by the inclusion of wheel odometry in the estimation procedures. As a case study, the proposed system is validated using the Pepper robot, whose short-range LIDARs and RGB-D camera do not allow the robot to self-localize in large environments. The localization system is tested in navigation tasks using Pepper in two different environments: a medium-size laboratory, and a large-size hall.
Visual-inertial SLAM is essential for robot navigation in GPS-denied environments, e.g. indoor, underground. Conventionally, the performance of visual-inertial SLAM is evaluated with open-loop analysis, with a focus on the drift level of SLAM systems. In this paper, we raise the question on the importance of visual estimation latency in closed-loop navigation tasks, such as accurate trajectory tracking. To understand the impact of both drift and latency on visual-inertial SLAM systems, a closed-loop benchmarking simulation is conducted, where a robot is commanded to follow a desired trajectory…
Applications involving autonomous navigation and planning of mobile agents can benefit greatly by employing online Simultaneous Localization and Mapping (SLAM) techniques, however, their proper implementation still warrants an efficient amalgamation with any offline path planning method that may be used for the particular application. In this paper, such a case of amalgamation is considered for a LiDAR-based indoor mapping system which presents itself as a 2D coverage path planning problem implemented along with online SLAM. This paper shows how classic offline Coverage Path Planning (CPP) can…
Institute of Industrial Science, The University of Tokyo（东京大学工业科学研究所）；Microsoft（微软）
Robot navigation technology is required to accomplish difficult tasks in various environments. In navigation, it is necessary to know the information of the external environments and the state of the robot under the environment. On the other hand, various studies have been done on SLAM technology, which is also used for navigation, but also applied to devices for Mixed Reality and the like. In this paper, we propose a robot-device calibration method for navigation with a device using SLAM technology on a robot. The calibration is performed by using the position and orientation information give…
Multicam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and Navigation
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Mechanical and Power Engineering, East China University of Science and Technology（华东理工大学机械与动力工程学院）
Shanghai Jiao Tong University（上海交通大学）；Sophia University（上智大学）；the Hong Kong University of Science and Technology（香港科技大学）；Hangzhou ATG Intelligent Technology Co. Ltd.（杭州ATG智能科技有限公司）
In this paper, we present an autonomous unmanned aerial vehicle (UAV) landing system based on visual navigation. We design the landmark as a topological pattern in order to enable the UAV to distinguish the landmark from the environment easily. In addition, a dynamic thresholding method is developed for image binarization to improve detection efficiency. The relative distance in the horizontal plane is calculated according to effective image information, and the relative height is obtained using a linear interpolation method. The landing experiments are performed on a static and a moving platf…
Centre for Automation and Robotics, Polytechnic University of Madrid（马德里理工大学自动化与机器人中心）
This article proposes a method to diminish the pose (position plus attitude) drift experienced by an SVO (Semi-Direct Visual Odometry) based visual navigation system installed onboard a UAV (Unmanned Air Vehicle) by supplementing its pose estimation non linear optimizations with priors based on the outputs of a GNSS (Global Navigation Satellite System) Denied inertial navigation system. The method is inspired in a PI (Proportional Integral) control system, in which the attitude, altitude, and rate of climb inertial outputs act as targets to ensure that the visual estimations do not deviate far…
IIIT Hyderabad（国际信息技术研究所海得拉巴分校）；University of Tartu（塔尔图大学）
We present UrbanFly: an uncertainty-aware real-time planning framework for quadrotor navigation in urban high-rise environments. A core aspect of UrbanFly is its ability to robustly plan directly on the sparse point clouds generated by a Monocular Visual Inertial SLAM (VINS) backend. It achieves this by using the sparse point clouds to build an uncertainty-integrated cuboid representation of the environment through a data-driven monocular plane segmentation network. Our chosen world model provides faster distance queries than the more common voxel-grid representation, and UrbanFly leverages th…
Hong Kong University of Science and Technology（香港科技大学）；Singapore Management University（新加坡管理大学）；Institute for Infocomm Research（新加坡资讯通信研究院）；Sun Yat-sen University（中山大学）
SLAM (Simultaneous Localization And Mapping) seeks to provide a moving agent with real-time self-localization. To achieve real-time speed, SLAM incrementally propagates position estimates. This makes SLAM fast but also makes it vulnerable to local pose estimation failures. As local pose estimation is ill-conditioned, local pose estimation failures happen regularly, making the overall SLAM system brittle. This paper attempts to correct this problem. We note that while local pose estimation is ill-conditioned, pose estimation over longer sequences is well-conditioned. Thus, local pose estimation…
SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation
Microsoft（微软公司）；GRASP Laboratory, University of Pennsylvania（宾夕法尼亚大学GRASP实验室）；University of Pennsylvania（宾夕法尼亚大学）；Department of Electrical and Computer Engineering, University of California San Diego（加州大学圣地亚哥分校电气与计算机工程系）
Light Detection and Ranging (LiDAR) based Simultaneous Localization and Mapping (SLAM) has drawn increasing interests in autonomous driving. However, LiDAR-SLAM suffers from accumulating errors which can be significantly mitigated by Global Navigation Satellite System (GNSS). Precise Point Positioning (PPP), an accurate GNSS operation mode independent of base stations, gains more popularity in unmanned systems. Considering the features of the two technologies, LiDAR-SLAM and PPP, this paper proposes a SLAM system, namely P3-LOAM (PPP based LiDAR Odometry and Mapping) which couples LiDAR-SLAM a…
Fully Onboard SLAM for Distributed Mapping with a Swarm of Nano-Drones
Department of Information Technology and Electrical Engineering (D-ITET), ETH Zürich（信息科技与电气工程系（D-ITET），苏黎世联邦理工学院）；Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna（电气、电子与信息工程系（DEI），博洛尼亚大学）
In this paper we propose an effective vision-based navigation method that allows a multirotor vehicle to simultaneously reach a desired goal pose in the environment while constantly facing a target object or landmark. Standard techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast maneuvers) do not allow to constantly maintain the line of sight with a target of interest. Instead, we compute the optimal trajectory by solving a non-linear optimization problem that minimizes the target re-project…
Northwestern Polytechnical University（西北工业大学）；City University of London（伦敦城市大学）
Autonomous navigation in unknown complex environment is still a hard problem, especially for small Unmanned Aerial Vehicles (UAVs) with limited computation resources. In this paper, a neural network-based reactive controller is proposed for a quadrotor to fly autonomously in unknown outdoor environment. The navigation controller makes use of only current sensor data to generate the control signal without any optimization or configuration space searching, which reduces both memory and computation requirement. The navigation problem is modelled as a Markov Decision Process (MDP) and solved using…
Technische Universität München（慕尼黑工业大学）；Oculus Research（奥库鲁斯研究院）；RWTH Aachen University（亚琛工业大学）
Micro aerial vehicles (MAVs) are strongly limited in their payload and power capacity. In order to implement autonomous navigation, algorithms are therefore desirable that use sensory equipment that is as small, low-weight, and low-power consuming as possible. In this paper, we propose a method for autonomous MAV navigation and exploration using a low-cost consumer-grade quadrocopter equipped with a monocular camera. Our vision-based navigation system builds on LSD-SLAM which estimates the MAV trajectory and a semi-dense reconstruction of the environment in real-time. Since LSD-SLAM only deter…
Massachusetts Institute of Technology (MIT)（麻省理工学院（MIT））；Northeastern University（东北大学）
Simultaneous localization and mapping (SLAM) is a critical capability in autonomous navigation, but in order to scale SLAM to the setting of "lifelong" SLAM, particularly under memory or computation constraints, a robot must be able to determine what information should be retained and what can safely be forgotten. In graph-based SLAM, the number of edges (measurements) in a pose graph determines both the memory requirements of storing a robot's observations and the computational expense of algorithms deployed for performing state estimation using those observations; both of which can grow unbo…
Control-Barrier-Aided Teleoperation with Visual-Inertial SLAM for Safe MAV Navigation in Complex Environments
Learning Systems and Robotics Lab, School of Computation, Information and Technology, Technical University of Munich（学习系统与机器人实验室，计算、信息与技术学院，慕尼黑技术大学）；Smart Robotics Lab, School of Computation, Information and Technology, Technical University of Munich（智能机器人实验室，计算、信息与技术学院，慕尼黑技术大学）；Smart Robotics Lab, Department of Computing, Imperial College London（智能机器人实验室，计算系，伦敦帝国理工学院）；Munich Institute of Robotics and Machine Intellig（慕尼黑机器人与机器智能研究所）
AERO-VIS: Asynchronous Event-based Real-time Onboard Visual-Inertial SLAM
Mobile Robotics Lab, ETH Zürich（苏黎世联邦理工学院移动机器人实验室）；Technical University of Munich（慕尼黑技术大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）
Visual Simultaneous Localization and Mapping (SLAM) systems are an essential component in agricultural robotics that enable autonomous navigation and the construction of accurate 3D maps of agricultural fields. However, lack of texture, varying illumination conditions, and lack of structure in the environment pose a challenge for Visual-SLAM systems that rely on traditional feature extraction and matching algorithms such as ORB or SIFT. This paper proposes 1) an object-level feature association algorithm that enables the creation of 3D reconstructions robustly by taking advantage of the struct…
FR-SLAM: A SLAM Improvement Method Based on Floor Plan Registration
Suzhou Campus Southeast University（东南大学苏州校区）；Nanjing center for applied mathematics（南京应用数学中心）；School of Automation（自动化学院）；School of Cyber Science and Engineering（网络科学与工程学院）；Shenzhen Research Institute Southeast University（东南大学深圳研究院）；Samsung Electronics（三星电子）；Samsung Electronics(China) R&D Center（三星电子（中国）研发中心）
VIO has been widely used and researched to control and aid the automation of navigation of robots especially in the absence of absolute position measurements, such as GPS. However, when observable landmarks in the scene lie far away from the robot's sensor suite, as it is the case at high altitude flights, the fidelity of estimates and the observability of the metric scale degrades greatly for these methods. Aiming to tackle this issue, in this article, we employ two UAVs equipped with one monocular camera and one IMU each, to exploit their view overlap and relative distance measurements betwe…
DynFly: Dynamic-Aware Continuous Trajectory Generation for UAV Vision-Language Navigation in Urban Environments
Beijing Institute of Technology（北京理工大学）；Tsinghua University（清华大学）；Dalian University of Technology（大连理工大学）；Xidian University（西安电子科技大学）；Huazhong University of Science and Technology（华中科技大学）
This paper proposes a novel simultaneous localization and mapping (SLAM) approach, namely Attention-SLAM, which simulates human navigation mode by combining a visual saliency model (SalNavNet) with traditional monocular visual SLAM. Most SLAM methods treat all the features extracted from the images as equal importance during the optimization process. However, the salient feature points in scenes have more significant influence during the human navigation process. Therefore, we first propose a visual saliency model called SalVavNet in which we introduce a correlation module and propose an adapt…
Towards Realistic UAV Vision-Language Navigation: Platform, Benchmark, and Methodology
Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；MMLab, CUHK（香港中文大学MMLab）；Centre for Perceptual and Interactive Intelligence（感知与交互智能中心）
Facebook AI Research（脸书人工智能研究院）；Carnegie Mellon University（卡内基梅隆大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）
This paper studies the problem of image-goal navigation which involves navigating to the location indicated by a goal image in a novel previously unseen environment. To tackle this problem, we design topological representations for space that effectively leverage semantics and afford approximate geometric reasoning. At the heart of our representations are nodes with associated semantic features, that are interconnected using coarse geometric information. We describe supervised learning-based algorithms that can build, maintain and use such representations under noisy actuation. Experimental st…
SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems
Guru Gobind Singh Indraprastha University（古鲁·戈宾德·辛格·因德拉普拉斯塔大学）；Delhi Skill and Entrepreneurship University (DSEU)（德里技能与创业大学（DSEU））；Delhi Institute of Tool Engineering（德里工具工程学院）
In the absence of external reference position information (e.g. GNSS) SLAM has proven to be an effective method for indoor navigation. The positioning drift can be reduced with regular loop-closures and global relaxation as the backend, thus achieving a good balance between exploration and exploitation. Although vision-based systems like laser scanners are typically deployed for SLAM, these sensors are heavy, energy inefficient, and expensive, making them unattractive for wearables or smartphone applications. However, the concept of SLAM can be extended to non-optical systems such as magnetome…
DEI - CNIT University of Bologna（博洛尼亚大学 DEI - CNIT）；CNR-IEIIT（意大利国家研究委员会电子、信息工程与电信研究所）；ECE Department Stony Brook University（石溪大学电子与计算机工程系）
In this paper, we study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown environment. The goal is to optimize its trajectory with the purpose of maximizing the mapping accuracy and, at the same time, to avoid areas where measurements might not be sufficiently informative from the perspective of a target detection. This problem is formulated as a Markov decision process (MDP) where the UAV is an agent that runs either a state estimator for target detection and for environment mapping, and a…
Exploring an unfamiliar indoor environment and avoiding obstacles is challenging for visually impaired people. Currently, several approaches achieve the avoidance of static obstacles based on the mapping of indoor scenes. To solve the issue of distinguishing dynamic obstacles, we propose an assistive system with an RGB-D sensor to detect dynamic information of a scene. Once the system captures an image, panoptic segmentation is performed to obtain the prior dynamic object information. With sparse feature points extracted from images and the depth information, poses of the user can be estimated…
