Recognizing already explored places (a.k.a. place recognition) is a fundamental task in Simultaneous Localization and Mapping (SLAM) to enable robot relocalization and loop closure detection. In topological SLAM the recognition takes place by comparing a signature (or feature vector) associated to the current node with the signatures of the nodes in the known map. However, as the number of nodes increases, matching the current node signature against all the existing ones becomes inefficient and thwarts real-time navigation. In this paper we propose a novel approach to pre-select a subset of map nodes for place recognition. The map nodes are clustered during exploration and each cluster is associated with a region. The region labels become the prediction targets of a deep neural network and, during navigation, only the nodes associated with the regions predicted with high probability are considered for matching. While the proposed technique can be integrated in different SLAM approaches, in this work we describe an effective integration with RTAB-Map (a popular framework for real-time topological SLAM) which allowed us to design and run several experiments to demonstrate its effectiveness. All the code and material from the experiments will be available online at https://github.com/MI-BioLab/region-learner.
Carnegie Mellon University（卡内基梅隆大学）；University of Freiburg（弗莱堡大学）
Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Markov localization is to maintain a probability density over the space of all locations of a robot in its environment. Our approach represents this space metrically, using a fine-grained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failur…
In order to fully function in human environments, robot perception will need to account for the uncertainty caused by translucent materials. Translucency poses several open challenges in the form of transparent objects (e.g., drinking glasses), refractive media (e.g., water), and diffuse partial occlusions (e.g., objects behind stained glass panels). This paper presents Plenoptic Monte Carlo Localization (PMCL) as a method for localizing object poses in the presence of translucency using plenoptic (light-field) observations. We propose a new depth descriptor, the Depth Likelihood Volume (DLV),…
Singapore University of Technology and Design（新加坡科技设计大学）；University of Jaffna（贾夫纳大学）；Southwest University of Science and Technology（西南科技大学）
Relative localization between autonomous robots without infrastructure is crucial to achieve their navigation, path planning, and formation in many applications, such as emergency response, where acquiring a prior knowledge of the environment is not possible. The traditional Ultra-WideBand (UWB)-based approach provides a good estimation of the distance between the robots, but obtaining the relative pose (including the displacement and orientation) remains challenging. We propose an approach to estimate the relative pose between a group of robots by equipping each robot with multiple UWB rangin…
University of Luxembourg（卢森堡大学）；Interdisciplinary Centre for Security, Reliability and Trust（安全、可靠性与信任跨学科中心）
In this paper, we propose a solution for legged robot localization using architectural plans. Our specific contributions towards this goal are several. Firstly, we develop a method for converting the plan of a building into what we denote as an architectural graph (A-Graph). When the robot starts moving in an environment, we assume it has no knowledge about it, and it estimates an online situational graph representation (S-Graph) of its surroundings. We develop a novel graph-to-graph matching method, in order to relate the S-Graph estimated online from the robot sensors and the A-Graph extract…
The University of Michigan（密歇根大学）；University of Washington（华盛顿大学）
Autonomous robot systems for applications from search and rescue to assistive guidance should be able to engage in natural language dialog with people. To study such cooperative communication, we introduce Robot Simultaneous Localization and Mapping with Natural Language (RobotSlang), a benchmark of 169 natural language dialogs between a human Driver controlling a robot and a human Commander providing guidance towards navigation goals. In each trial, the pair first cooperates to localize the robot on a global map visible to the Commander, then the Driver follows Commander instructions to move …
Ritsumeikan University（立命馆大学）；National Institute of Informatics（国立信息学研究所）；The Graduate University for Advanced Studies（综合研究大学院大学）
In this paper, we propose a novel unsupervised learning method for the lexical acquisition of words related to places visited by robots, from human continuous speech signals. We address the problem of learning novel words by a robot that has no prior knowledge of these words except for a primitive acoustic model. Further, we propose a method that allows a robot to effectively use the learned words and their meanings for self-localization tasks. The proposed method is nonparametric Bayesian spatial concept acquisition method (SpCoA) that integrates the generative model for self-localization and…
Recent advances in robotic mobile manipulation have spurred the expansion of the operating environment for robots from constrained workspaces to large-scale, human environments. In order to effectively complete tasks in these spaces, robots must be able to perceive, reason, and execute over a diversity of affordances, well beyond simple pick-and-place. We posit the notion of semantic frames provides a compelling representation for robot actions that is amenable to action-focused perception, task-level reasoning, action-level execution, and integration with language. Semantic frames, a product …
Harbin Institute of Technology Shenzhen Graduate School（哈尔滨工业大学深圳研究生院）；University of Utah（犹他大学）
A new 3D localization and mapping techinque with terrain inclination assistance is proposed in this paper to allow a robot to identify its location and build a global map in an outdoor environment. The Iterative Closest Points (ICP) algorithm and terrain inclination-based localization are combined together to achieve accurate and fast localization and mapping. Inclinations of the terrains the robot navigates are used to achieve local localization during the interval between two laser scans. Using the results of the above localization as the initial condition, the ICP algorithm is then applied …
Continuum Robot Localization using Distributed Time-of-Flight Sensors
University of Toronto Robotics Institute, Canada（多伦多大学机器人研究所，加拿大）；Oxford Robotics Institute, University of Oxford, UK（牛津大学机器人研究所，英国）；Toronto Metropolitan University, Canada（多伦多 Metropolitan 大学，加拿大）
South China University of Technology（华南理工大学）；PerceptIn
Robotics systems are complex, often consisted of basic services including SLAM for localization and mapping, Convolution Neural Networks for scene understanding, and Speech Recognition for user interaction, etc. Meanwhile, robots are mobile and usually have tight energy constraints, integrating these services onto an embedded platform with around 10 W of power consumption is critical to the proliferation of mobile robots. In this paper, we present a case study on integrating real-time localization, vision, and speech recognition services on a mobile SoC, Nvidia Jetson TX1, within about 10 W of…
National Institute of Technology, Raipur（印度国立理工学院赖布尔分校）；Motilal Nehru National Institute of Technology, Allahabad（莫蒂拉尔·尼赫鲁国立理工学院阿拉哈巴德分校）；Indian Institute of Information Technology, Allahabad（印度信息技术学院阿拉哈巴德分校）
Cloud Robotics is one of the emerging area of robotics. It has created a lot of attention due to its direct practical implications on Robotics. In Cloud Robotics, the concept of cloud computing is used to offload computational extensive jobs of the robots to the cloud. Apart from this, additional functionalities can also be offered on run to the robots on demand. Simultaneous Localization and Mapping (SLAM) is one of the computational intensive algorithm in robotics used by robots for navigation and map building in an unknown environment. Several Cloud based frameworks are proposed specificall…
A Survey on Global LiDAR Localization: Challenges, Advances and Open Problems
Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University（浙江大学）；National University of Defense Technology（国防科技大学）；University of Bonn（波恩大学）
This paper mainly studies the localization and mapping of range sensing robots in the confidence-rich map (CRM) and then extends it to provide a full state estimate for information-theoretic exploration. Most previous works about active simultaneous localization and mapping and exploration always assumed the known robot poses or utilized inaccurate information metrics to approximate pose uncertainty, resulting in imbalanced exploration performance and efficiency in the unknown environment. This inspires us to extend the confidence-rich mutual information (CRMI) with measurable pose uncertainty…
Robots deployed in settings such as warehouses and parking lots must cope with frequent and substantial changes when localizing in their environments. While many previous localization and mapping algorithms have explored methods of identifying and focusing on long-term features to handle change in such environments, we propose a different approach -- can a robot understand the distribution of movable objects and relate it to observations of such objects to reason about global localization? In this paper, we present probabilistic object maps (POMs), which represent the distributions of movable …
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.
Traditional simultaneous localization and mapping (SLAM) methods focus on improvement in the robot's localization under environment and sensor uncertainty. This paper, however, focuses on mitigating the need for exact localization of a mobile robot to pursue autonomous navigation using a sparse set of images. The proposed method consists of a model architecture - RoomNet, for unsupervised learning resulting in a coarse identification of the environment and a separate local navigation policy for local identification and navigation. The former learns and predicts the scene based on the short ter…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
In this study, we propose a novel visual localization approach to accurately estimate six degrees of freedom (6-DoF) poses of the robot within the 3D LiDAR map based on visual data from an RGB camera. The 3D map is obtained utilizing an advanced LiDAR-based simultaneous localization and mapping (SLAM) algorithm capable of collecting a precise sparse map. The features extracted from the camera images are compared with the points of the 3D map, and then the geometric optimization problem is being solved to achieve precise visual localization. Our approach allows employing a scout robot equipped …
The Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL). The difficulties are two-fold. The first is the difficulty of global localization for significant viewpoint difference. Appearance-based localization methods tend to fail under large viewpoint changes. Recently, semantic graphs have been utilized to overcome the viewpoint variation problem. However, the methods are highly time-consuming, especially in large-scale environments. This leads to the second difficulty, which is h…
Monocular Vision-Based Swarm Robot Localization Using Equilateral Triangular Formations
School of Integrated Technology, Yonsei University（延世大学整合技术学院）；Korea Institute of Robotics and Technology Convergence（韩国机器人与技术融合研究院）；DOGU, Inc.（DOGU公司）
Technion - Israel Institute of Technology（以色列理工学院）
We present an approach for multi-robot consistent distributed localization and semantic mapping in an unknown environment, considering scenarios with classification ambiguity, where objects' visual appearance generally varies with viewpoint. Our approach addresses such a setting by maintaining a distributed posterior hybrid belief over continuous localization and discrete classification variables. In particular, we utilize a viewpoint-dependent classifier model to leverage the coupling between semantics and geometry. Moreover, our approach yields a consistent estimation of both continuous and …
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the community. Yet collaborating SLAM over multiple robots still remains challenging due to performance contradiction between the intensive graphics computation of SLAM and the limited computing capability of robots. While traditional solutions resort to the powerful cloud servers acting as an external computation provider, we show by real-world measurements that the significant communication overhead in data offloading prevents…
University of Lincoln（林肯大学）；Saga Robotics（萨迦机器人公司）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
The agricultural domain offers a working environment where many human laborers are nowadays employed to maintain or harvest crops, with huge potential for productivity gains through the introduction of robotic automation. Detecting and localizing humans reliably and accurately in such an environment, however, is a prerequisite to many services offered by fleets of mobile robots collaborating with human workers. Consequently, in this paper, we expand on the concept of a topological particle filter (TPF) to accurately and individually localize and track workers in a farm environment, integrating…
Semantic mapping is the task of providing a robot with a map of its environment beyond the open, navigable space of traditional Simultaneous Localization and Mapping (SLAM) algorithms by attaching semantics to locations. The system presented in this work reads door placards to annotate the locations of offices. Whereas prior work on this system developed hand-crafted detectors, this system leverages YOLOv5 for sign detection and EAST for text recognition. Placards are localized by computing their pose from a point cloud in a RGB-D camera frame localized by a modified ORB-SLAM. Semantic mapping…
Multi-robot Systems Group, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague（布拉格捷克技术大学电气工程学院控制学系多机器人系统组）；Turku Intelligent Embedded and Robotic Systems (TIERS) Lab, University of Turku（图尔库大学智能嵌入式与机器人系统实验室）
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…
Queen Mary University of London（伦敦玛丽女王大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
A novel simultaneous localization and radio mapping (SLARM) framework for communication-aware connected robots in the unknown indoor environment is proposed, where the simultaneous localization and mapping (SLAM) algorithm and the global geographic map recovery (GGMR) algorithm are leveraged to simultaneously construct a geographic map and a radio map named a channel power gain map. Specifically, the geographic map contains the information of a precise layout of obstacles and passable regions, and the radio map characterizes the position-dependent maximum expected channel power gain between th…
The LiDAR and inertial sensors based localization and mapping are of great significance for Unmanned Ground Vehicle related applications. In this work, we have developed an improved LiDAR-inertial localization and mapping system for unmanned ground vehicles, which is appropriate for versatile search and rescue applications. Compared with existing LiDAR-based localization and mapping systems such as LOAM, we have two major contributions: the first is the improvement of the robustness of particle swarm filter-based LiDAR SLAM, while the second is the loop closure methods developed for global opt…
We present the first fully distributed multi-robot system for dense metric-semantic Simultaneous Localization and Mapping (SLAM). Our system, dubbed Kimera-Multi, is implemented by a team of robots equipped with visual-inertial sensors, and builds a 3D mesh model of the environment in real-time, where each face of the mesh is annotated with a semantic label (e.g., building, road, objects). In Kimera-Multi, each robot builds a local trajectory estimate and a local mesh using Kimera. Then, when two robots are within communication range, they initiate a distributed place recognition and robust po…
University of Florida（佛罗里达大学）；University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）
We present the concept of concurrent flow-based localization and mapping (FLAM) for autonomous field robots navigating within background flows. Different from the classical simultaneous localization and mapping (SLAM) problem, where the robot interacts with discrete features, FLAM utilizes the continuous flow fields as navigation references for mobile robots and provides flow field mapping capability with in-situ flow velocity observations. This approach is of importance to underwater vehicles in mid-depth oceans or aerial vehicles in GPS-denied atmospheric circulations. This article introduce…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Kharkevich Institute for Information Transmission Problems RAS（哈尔克维奇信息传输问题研究所（俄罗斯科学院））
An appearance-based robot self-localization problem is considered in the machine learning framework. The appearance space is composed of all possible images, which can be captured by a robot's visual system under all robot localizations. Using recent manifold learning and deep learning techniques, we propose a new geometrically motivated solution based on training data consisting of a finite set of images captured in known locations of the robot. The solution includes estimation of the robot localization mapping from the appearance space to the robot localization space, as well as estimation o…
Colorado School of Mines（科罗拉多矿业学院）；Toyota Motor North America（丰田汽车北美公司）
Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations from multiple perspectives with reliant cooperation. To enable collaborative localization, four key challenges must be addressed, including modeling complex relationships between observed objects, fusing observations from an arbitrary number of collaborating robots, quantifying localization uncertainty, and addressing latency of robot communications. In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph …
Robot localization is a one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we are able to localize the robot in the …
We demonstrated multi-mobile robot navigation based on Visible Light Positioning(VLP) localization. From our experiment, the VLP can accurately locate robots' positions in navigation.
The University of Michigan - Ann Arbor（密歇根大学安娜堡分校）
In this paper, we introduce a set-theoretic approach for mobile robot localization with infrastructure-based sensing. The proposed method computes sets that over-bound the robot body and orientation under an assumption of known noise bounds on the sensor and robot motion model. We establish theoretical properties and computational approaches for this set-theoretic localization approach and illustrate its application to an automated valet parking example in simulations and to omnidirectional robot localization problems in real-world experiments. We demonstrate that the set-theoretic localizatio…
TECNALIA, Basque Research and Technology Alliance (BRTA)（TECNALIA，巴斯克研究与技术联盟）；University of the Basque Country (UPV-EHU)（巴斯克国家大学）；Robotics and Autonomous Systems Group (RSAIT)（机器人与自主系统组）
Reliability is a key factor for realizing safety guarantee of full autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick re-localization, simultaneously. The presented method can be implemented using similar estimation manner to that of …
National University of Singapore（新加坡国立大学）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）
Central to robot exploration and mapping is the task of persistent localization in environmental fields characterized by spatially correlated measurements. This paper presents a Gaussian process localization (GP-Localize) algorithm that, in contrast to existing works, can exploit the spatially correlated field measurements taken during a robot's exploration (instead of relying on prior training data) for efficiently and scalably learning the GP observation model online through our proposed novel online sparse GP. As a result, GP-Localize is capable of achieving constant time and memory (i.e., …
University of Freiburg（弗莱堡大学）；Toyota Research Institute（丰田研究院）
Indoor localization is one of the crucial enablers for deployment of service robots. Although several successful techniques for indoor localization have been proposed, the majority of them relies on maps generated from data gathered with the same sensor modality used for localization. Typically, tedious labor by experts is needed to acquire this data, thus limiting the readiness of the system as well as its ease of installation for inexperienced operators. In this paper, we propose a memory and computationally efficient monocular camera-based localization system that allows a robot to estimate…
Lafayette College（拉斐特学院）；University of Pennsylvania（宾夕法尼亚大学）
A key limitation of current multi-robot systems is a lack of relative localization, particularly in environments without GPS or motion capture systems. This article presents a centralized method for relatively localizing a 2D swarm using sensors and beacons on the robots themselves. The UKF-based algorithm as well as the requisite novel and cost-effective sensing hardware are discussed. Comparisons with a motion capture system show that the method is capable of localization with errors on the order of the size of the robots.
Institute for Aerospace Studies, University of Toronto（多伦多大学航空航天研究所）；Vector Institute for Artificial Intelligence（矢量人工智能研究院）
Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization is a promising lightweight, low-cost solution that can scale to a large number of devices -- making it especially suited for resource-constrained multi-robot applications. However, the localization accuracy of standard, commercially available UWB radios is often insufficient due to significant measurement bias and outliers. In this letter, we address these issues by proposing a robu…
This paper presents a localization system for mobile robots enabling precise localization in inaccurate building models. The approach leverages local referencing to counteract inherent deviations between as-planned and as-built data for locally accurate registration. We further fuse a novel image-based robust outlier detector with LiDAR data to reject a wide range of outlier measurements from clutter, dynamic objects, and sensor failures. We evaluate the proposed approach on a mobile robot in a challenging real world building construction site. It consistently outperforms the traditional ICP-b…
Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD maps is highly relevant for the current advances in autonomous driving, but remains a challenging topic in research. We present a novel monocular localization approach based on a sliding-window pose graph that leverages predicted uncertainties for increased precision and robustness against challenging scenarios and per frame failures. To this end, we propose an efficient multi-task uncertainty-aware perception module, which covers semantic segmentation, as well as bounding box detection, to enable the loca…
Universidad de Sevilla（塞维利亚大学）；Universidad Pablo de Olavide（巴勃罗·德·奥拉维德大学）
This paper presents DLL, a fast direct map-based localization technique using 3D LIDAR for its application to aerial robots. DLL implements a point cloud to map registration based on non-linear optimization of the distance of the points and the map, thus not requiring features, neither point correspondences. Given an initial pose, the method is able to track the pose of the robot by refining the predicted pose from odometry. Through benchmarks using real datasets and simulations, we show how the method performs much better than Monte-Carlo localization methods and achieves comparable precision…
Ultra-wideband (UWB) ranging has emerged as a key radio technology for robot positioning and relative localization in multi-robot systems. Multiple works are now advancing towards more scalable systems, but challenges still remain. This paper proposes a novel approach to relative localization in multi-robot systems where the roles of the UWB nodes are dynamically allocated between active nodes (using time-of-flight for ranging estimation to other active nodes) and passive nodes (using time-difference-of-arrival for estimating range differences with respect to pairs of active nodes). We adaptiv…
