Robots operating in multi-player settings must simultaneously model the environment and the behavior of human or robotic agents who share that environment. This modeling is often approached using Simultaneous Localization and Mapping (SLAM); however, SLAM algorithms usually neglect multi-player interactions. In contrast, the motion planning literature often uses dynamic game theory to explicitly model noncooperative interactions of multiple agents in a known environment with perfect localization. Here, we present GTP-SLAM, a novel, iterative best response-based SLAM algorithm that accurately performs state localization and map reconstruction, while using game theoretic priors to capture the inherent non-cooperative interactions among multiple agents in an uncharted scene. By formulating the underlying SLAM problem as a potential game, we inherit a strong convergence guarantee. Empirical results indicate that, when deployed in a realistic traffic simulation, our approach performs localization and mapping more accurately than a standard bundle adjustment algorithm across a wide range of noise levels.
Simultaneous Localization and Mapping (SLAM) is one of the key robotics tasks as it tackles simultaneous mapping of the unknown environment defined by multiple landmark positions and localization of the unknown pose (i.e., attitude and position) of the robot in three-dimensional (3D) space. The true SLAM problem is modeled on the Lie group of $\mathbb{SLAM}_{n}\left(3\right)$, and its true dynamics rely on angular and translational velocities. This paper proposes a novel geometric nonlinear stochastic estimator algorithm for SLAM on $\mathbb{SLAM}_{n}\left(3\right)$ that precisely mimics the n…
Thompson Rivers University（汤普森河大学）；The Hong Kong Polytechnic University（香港理工大学）
Simultaneous Localization and Mapping (SLAM) is a process of concurrent estimation of the vehicle's pose and feature locations with respect to a frame of reference. This paper proposes a computationally cheap geometric nonlinear SLAM filter algorithm structured to mimic the nonlinear motion dynamics of the true SLAM problem posed on the matrix Lie group of $\mathbb{SLAM}_{n}\left(3\right)$. The nonlinear filter on manifold is proposed in continuous form and it utilizes available measurements obtained from group velocity vectors, feature measurements and an inertial measurement unit (IMU). The …
University of Freiburg（弗赖堡大学）；University of Technology Nuremberg（纽伦堡工业大学）
Robots operating in the open world encounter various different environments that can substantially differ from each other. This domain gap also poses a challenge for Simultaneous Localization and Mapping (SLAM) being one of the fundamental tasks for navigation. In particular, learning-based SLAM methods are known to generalize poorly to unseen environments hindering their general adoption. In this work, we introduce the novel task of continual SLAM extending the concept of lifelong SLAM from a single dynamically changing environment to sequential deployments in several drastically differing en…
Simultaneous Localization and Mapping (SLAM) algorithms are frequently deployed to support a wide range of robotics applications, such as autonomous navigation in unknown environments, and scene mapping in virtual reality. Many of these applications require autonomous agents to perform SLAM in highly dynamic scenes. To this end, this tutorial extends a recently introduced, unifying optimization-based SLAM backend framework to environments with moving objects and features. Using this framework, we consider a rapprochement of recent advances in dynamic SLAM. Moreover, we present dynamic EKF SLAM…
Simultaneous Localization and Mapping (SLAM) achieves the purpose of simultaneous positioning and map construction based on self-perception. The paper makes an overview in SLAM including Lidar SLAM, visual SLAM, and their fusion. For Lidar or visual SLAM, the survey illustrates the basic type and product of sensors, open source system in sort and history, deep learning embedded, the challenge and future. Additionally, visual inertial odometry is supplemented. For Lidar and visual fused SLAM, the paper highlights the multi-sensors calibration, the fusion in hardware, data, task layer. The open …
This paper implements Simultaneous Localization and Mapping (SLAM) technique to construct a map of a given environment. A Real Time Appearance Based Mapping (RTAB-Map) approach was taken for accomplishing this task. Initially, a 2d occupancy grid and 3d octomap was created from a provided simulated environment. Next, a personal simulated environment was created for mapping as well. In this appearance based method, a process called Loop Closure is used to determine whether a robot has seen a location before or not. In this paper, it is seen that RTAB-Map is optimized for large scale and long te…
A robust nonlinear stochastic observer for simultaneous localization and mapping (SLAM) is proposed using the available uncertain measurements of angular velocity, translational velocity, and features. The proposed observer is posed on the Lie Group of $\mathbb{SLAM}_{n}\left(3\right)$ to mimic the true stochastic SLAM dynamics. The proposed approach considers the velocity measurements to be attached with an unknown bias and an unknown Gaussian noise. The proposed SLAM observer ensures that the closed loop error signals are semi-globally uniformly ultimately bounded. Simulation results demonst…
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…
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Environment perception is a crucial ability for robot's interaction into an environment. One of the first steps in this direction is the combined problem of simultaneous localization and mapping (SLAM). A new method, called G-SLAM, is proposed, where the map is considered as a set of scattered points in the continuous space followed by a probability that states the existence of an obstacle in the subsequent point in space. A probabilistic approach with particle filters for the robot's pose estimation and an adaptive recursive algorithm for the map's probability distribution estimation is prese…
Existing solutions to visual simultaneous localization and mapping (V-SLAM) assume that errors in feature extraction and matching are independent and identically distributed (i.i.d), but this assumption is known to not be true -- features extracted from low-contrast regions of images exhibit wider error distributions than features from sharp corners. Furthermore, V-SLAM algorithms are prone to catastrophic tracking failures when sensed images include challenging conditions such as specular reflections, lens flare, or shadows of dynamic objects. To address such failures, previous work has focus…
ETH Zürich（苏黎世联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）；Universidad Sergio Arboleda（塞尔吉奥·阿博莱达大学）；Pontificia Universidad Javeriana（哈维里亚那天主教大学）；University of Adelaide（阿德莱德大学）；Australian Center for Robotic Vision（澳大利亚机器人视觉中心）
Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term …
The Hong Kong Polytechnic University（香港理工大学）；Zhejiang University of Science and Technology（浙江科技学院）
Most current LiDAR simultaneous localization and mapping (SLAM) systems build maps in point clouds, which are sparse when zoomed in, even though they seem dense to human eyes. Dense maps are essential for robotic applications, such as map-based navigation. Due to the low memory cost, mesh has become an attractive dense model for mapping in recent years. However, existing methods usually produce mesh maps by using an offline post-processing step to generate mesh maps. This two-step pipeline does not allow these methods to use the built mesh maps online and to enable localization and meshing to …
Polytechnique Montréal（蒙特利尔理工学院）；University of Oxford（牛津大学）；Desay SV Automotive（德赛西威汽车电子）
Motivated by the tremendous progress we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of…
Doppler-SLAM: Doppler-Aided Radar-Inertial and LiDAR-Inertial Simultaneous Localization and Mapping
Julius-Maximilians-Universität Würzburg（乌尔姆-马克斯-普朗克大学）；Zentrum für Telematik e.V.（电信研究中心）；Nuremberg Institute of Technology Georg Simon Ohm（纽伦堡技术学院 Georg Simon Ohm）；CARIAD SE（CARIAD SE公司）；University of Bonn（波恩大学）；Center for Robotics（机器人中心）；Lamarr Institute for Machine Learning and Artificial Intelligence（Lamarr机器学习与人工智能研究所）
Southern University of Science and Technology（南方科技大学）
Structured Light Illumination (SLI) systems have been used for reliable indoor dense 3D scanning via phase triangulation. However, mobile SLI systems for 360 degree 3D reconstruction demand 3D point cloud registration, involving high computational complexity. In this paper, we propose a phase based Simultaneous Localization and Mapping (Phase-SLAM) framework for fast and accurate SLI sensor pose estimation and 3D object reconstruction. The novelty of this work is threefold: (1) developing a reprojection model from 3D points to 2D phase data towards phase registration with low computational com…
Nanjing University of Science and Technology（南京理工大学）
Simultaneous Localization and Mapping (SLAM) plays an important role in outdoor and indoor applications ranging from autonomous driving to indoor robotics. Outdoor SLAM has been widely used with the assistance of LiDAR or GPS. For indoor applications, the LiDAR technique does not satisfy the accuracy requirement and the GPS signals will be lost. An accurate and efficient scene sensing technique is required for indoor SLAM. As the most promising 3D sensing technique, the opportunities for indoor SLAM with fringe projection profilometry (FPP) systems are obvious, but methods to date have not ful…
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
Simultaneous Localization and Mapping (SLAM) is a fundamental task to mobile and aerial robotics. LiDAR based systems have proven to be superior compared to vision based systems due to its accuracy and robustness. In spite of its superiority, pure LiDAR based systems fail in certain degenerate cases like traveling through a tunnel. We propose Stereo Visual Inertial LiDAR (VIL) SLAM that performs better on these degenerate cases and has comparable performance on all other cases. VIL-SLAM accomplishes this by incorporating tightly-coupled stereo visual inertial odometry (VIO) with LiDAR mapping …
Imperial College London（伦敦帝国理工学院）；Dyson Technology Ltd.（戴森科技有限公司）
There are many possibilities for how to represent the map in simultaneous localisation and mapping (SLAM). While sparse, keypoint-based SLAM systems have achieved impressive levels of accuracy and robustness, their maps may not be suitable for many robotic tasks. Dense SLAM systems are capable of producing dense reconstructions, but can be computationally expensive and, like sparse systems, lack higher-level information about the structure of a scene. Human-made environments contain a lot of structure, and we seek to take advantage of this by enabling the use of quadric surfaces as features in…
This paper proposes an A*SLAM system that features combining two sets of fisheye stereo cameras and taking the image edge as the SLAM features. The dual fisheye stereo camera sets cover the full environmental view of the SLAM system. From each fisheye stereo image pair, a panorama depth image can be directly extracted for initializing the SLAM feature. The edge feature is an illumination invariant feature. The paper presents a method of the edge-based simultaneous localization and mapping process using both the normal and inverted images interchangeably.
Simultaneous Localization And Mapping (SLAM) is the problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. How to enable SLAM robustly and durably on mobile, or even IoT grade devices, is the main challenge faced by the industry today. The main problems we need to address are: 1.) how to accelerate the SLAM pipeline to meet real-time requirements; and 2.) how to reduce SLAM energy consumption to extend battery life. After delving into the problem, we found out that feature extraction is indeed the bottleneck of p…
Chuo University（中央大学）；Chiba Institute of Technology（千叶工业大学）；Tokyo Polytechnic University（东京工艺大学）
This paper describes a novel SLAM (simultaneous localization and mapping) scheme based on scan matching in an environment including various physical properties.
Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previously unseen environment while simultaneously localising itself with respect to the map. In particular, Visual-SLAM uses various sensors from the mobile robot for collecting and sensing a representation of the map. Traditionally, geometric model-based techniques were used to tackle the SLAM problem, which tends to be error-prone under challenging environments. Recent advancements in computer vision, such as deep learning techniques, have provided…
Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle …
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Artificial Intelligence Research Institute（人工智能研究院）；Sber AI Lab（Sber人工智能实验室）
This work is dedicated to simultaneous continuous-time trajectory estimation and mapping based on Gaussian Processes (GP). State-of-the-art GP-based models for Simultaneous Localization and Mapping (SLAM) are computationally efficient but can only be used with a restricted class of kernel functions. This paper provides the algorithm based on GP with Random Fourier Features (RFF) approximation for SLAM without any constraints. The advantages of RFF for continuous-time SLAM are that we can consider a broader class of kernels and, at the same time, maintain computational complexity at reasonably …
Simultaneous localization and mapping (SLAM) is used to predict the dynamic motion path of a moving platform based on the location coordinates and the precise mapping of the physical environment. SLAM has great potential in augmented reality (AR), autonomous vehicles, viz. self-driving cars, drones, Autonomous navigation robots (ANR). This work introduces a hybrid learning model that explores beyond feature fusion and conducts a multimodal weight sewing strategy towards improving the performance of a baseline SLAM algorithm. It carries out weight enhancement of the front end feature extractor …
A reliable pose estimator robust to environmental disturbances is desirable for mobile robots. To this end, inertial measurement units (IMUs) play an important role because they can perceive the full motion state of the vehicle independently. However, it suffers from accumulative error due to inherent noise and bias instability, especially for low-cost sensors. In our previous studies on Wheel-INS \cite{niu2021, wu2021}, we proposed to limit the error drift of the pure inertial navigation system (INS) by mounting an IMU to the wheel of the robot to take advantage of rotation modulation. Howeve…
In this work, we propose a simultaneous localization and mapping (SLAM) system using a monocular camera and Ultra-wideband (UWB) sensors. Our system, referred to as VRSLAM, is a multi-stage framework that leverages the strengths and compensates for the weaknesses of each sensor. Firstly, we introduce a UWB-aided 7 degree-of-freedom (scale factor, 3D position, and 3D orientation) global alignment module to initialize the visual odometry (VO) system in the world frame defined by the UWB anchors. This module loosely fuses up-to-scale VO and ranging data using either a quadratically constrained qu…
ARC Centre of Excellence for Robotic Vision（ARC机器人视觉卓越中心）；Queensland University of Technology (QUT)（昆士兰科技大学）
Research in Simultaneous Localization And Mapping (SLAM) is increasingly moving towards richer world representations involving objects and high level features that enable a semantic model of the world for robots, potentially leading to a more meaningful set of robot-world interactions. Many of these advances are grounded in state-of-the-art computer vision techniques primarily developed in the context of image-based benchmark datasets, leaving several challenges to be addressed in adapting them for use in robotics. In this paper, we derive a formulation for Simultaneous Localization And Mappin…
Simultaneous localisation and mapping (SLAM) is the problem of autonomous robots to construct or update a map of an undetermined unstructured environment while simultaneously estimate the pose in it. The current trend towards self-driving vehicles has influenced the development of robust SLAM techniques over the last 30 years. This problem is addressed by using a standard sensor or a sensor array (Ultrasonic sensor, LIDAR, Camera, Kinect RGB-D) with sensor fusion techniques to achieve the perception step. Sensing method is determined by considering the specifications of the environment to extr…
The Simultaneous Localization and Mapping (SLAM) problem addresses the possibility of a robot to localize itself in an unknown environment and simultaneously build a consistent map of this environment. Recently, cameras have been successfully used to get the environment's features to perform SLAM, which is referred to as visual SLAM (VSLAM). However, classical VSLAM algorithms can be easily induced to fail when either the motion of the robot or the environment is too challenging. Although new approaches based on Deep Neural Networks (DNNs) have achieved promising results in VSLAM, they still a…
Saint-Petersburg Electrotechnical University “LETI”（圣彼得堡电工大学）；Massachusetts Institute of Technology（麻省理工学院）
SLAM (Simultaneous Localization and mapping) is one of the most challenging problems for mobile platforms and there is a huge amount of modern SLAM algorithms. The choice of the algorithm that might be used in every particular problem requires prior knowledge about advantages and disadvantages of each algorithm. This paper presents the approach for comparison of SLAM algorithms that allows to find the most accurate one. The accent of research is made on 2D SLAM algorithms and the focus of analysis is 2D map that is built after algorithm performance. Three metrics for evaluation of maps are pre…
Massachusetts Institute of Technology（麻省理工学院）；Laboratory for Information and Decision Systems（信息与决策系统实验室）；Computer Science and Artificial Intelligence Laboratory（计算机科学与人工智能实验室）
Simultaneous localization and mapping (SLAM) is the process of constructing a global model of an environment from local observations of it; this is a foundational capability for mobile robots, supporting such core functions as planning, navigation, and control. This article reviews recent progress in SLAM, focusing on advances in the expressive capacity of the environmental models used in SLAM systems (representation) and the performance of the algorithms used to estimate these models from data (inference). A prominent theme of recent SLAM research is the pursuit of environmental representatio…
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…
Alibaba Group（阿里巴巴集团）；Simon Fraser University（西蒙弗雷泽大学）；Hong Kong University of Science and Technology（香港科技大学）
There is an emerging trend of using neural implicit functions for map representation in Simultaneous Localization and Mapping (SLAM). Some pioneer works have achieved encouraging results on RGB-D SLAM. In this paper, we present a dense RGB SLAM method with neural implicit map representation. To reach this challenging goal without depth input, we introduce a hierarchical feature volume to facilitate the implicit map decoder. This design effectively fuses shape cues across different scales to facilitate map reconstruction. Our method simultaneously solves the camera motion and the neural implici…
College of Information and Computer Sciences（信息与计算机科学学院）
Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users. For such scenarios, where state-of-the-art mapping algorithms produce globally inconsistent maps, we introduce a systematic approach to incorporating sparse human corrections, which we term Human-in-the-Loop Simultaneous Localization and Mapping (HitL-SLAM). Given an initial factor graph for pose graph SLAM, HitL-SLAM accepts approximate, potentially erroneous, and rank-deficient human input, infers the …
This paper solves the classical problem of simultaneous localization and mapping (SLAM) in a fashion which avoids linearized approximations altogether. Based on creating virtual synthetic measurements, the algorithm uses a linear time- varying (LTV) Kalman observer, bypassing errors and approximations brought by the linearization process in traditional extended Kalman filtering (EKF) SLAM. Convergence rates of the algorithm are established using contraction analysis. Different combinations of sensor information can be exploited, such as bearing measurements, range measurements, optical flow, o…
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Medical Institutions（约翰斯·霍普金斯医疗机构）；Galen Robotics, Inc（盖伦机器人公司）
In endoscopy, many applications (e.g., surgical navigation) would benefit from a real-time method that can simultaneously track the endoscope and reconstruct the dense 3D geometry of the observed anatomy from a monocular endoscopic video. To this end, we develop a Simultaneous Localization and Mapping system by combining the learning-based appearance and optimizable geometry priors and factor graph optimization. The appearance and geometry priors are explicitly learned in an end-to-end differentiable training pipeline to master the task of pair-wise image alignment, one of the core components …
Sapienza University of Rome（罗马大学）；Robert Bosch Corporate Research（罗伯特·博世企业研究部）
Nowadays, SLAM (Simultaneous Localization and Mapping) is considered by the Robotics community to be a mature field. Currently, there are many open-source systems that are able to deliver fast and accurate estimation in typical real-world scenarios. Still, all these systems often provide an ad-hoc implementation that entailed to predefined sensor configurations. In this work, we tackle this issue, proposing a novel SLAM architecture specifically designed to address heterogeneous sensors' configuration and to standardize SLAM solutions. Thanks to its modularity and to specific design patterns, …
