There is a general expectation that robots should operate in urban environments often consisting of potentially dynamic entities including people, furniture and automobiles. Dynamic objects pose challenges to visual SLAM algorithms by introducing errors into the front-end. This paper presents a Light Field SLAM front-end which is robust to dynamic environments. A Light Field captures a bundle of light rays emerging from a single point in space, allowing us to see through dynamic objects occluding the static background via Synthetic Aperture Imaging(SAI). We detect apriori dynamic objects using semantic segmentation and perform semantic guided SAI on the Light Field acquired from a linear camera array. We simultaneously estimate both the depth map and the refocused image of the static background in a single step eliminating the need for static scene initialization. The GPU implementation of the algorithm facilitates running at close to real time speeds of 4 fps. We demonstrate that our method results in improved robustness and accuracy of pose estimation in dynamic environments by comparing it with state of the art SLAM algorithms.
Accurate 3D point cloud map generation is a core task for various robot missions or even for data-driven urban analysis. To do so, light detection and ranging (LiDAR) sensor-based simultaneous localization and mapping (SLAM) technology have been elaborated. To compose a full SLAM system, many odometry and place recognition methods have independently been proposed in academia. However, they have hardly been integrated or too tightly combined so that exchanging (upgrading) either single odometry or place recognition module is very effort demanding. Recently, the performance of each module has be…
In autonomous robot exploration, the frontier is the border in the world map between the explored space and unexplored space. The frontier plays an important role when deciding where in the environment the robots should go explore next. We examine a modular control system pipeline for autonomous exploration where a 2D graph SLAM algorithm based on occupancy grid submaps performs map building and localization. We provide an overview of the state of the art in frontier detection and the relevant SLAM concepts and propose a specialized frontier detection method which is efficiently constrained to…
Recent achievements in depth prediction from a single RGB image have powered the new research area of combining convolutional neural networks (CNNs) with classical simultaneous localization and mapping (SLAM) algorithms. The depth prediction from a CNN provides a reasonable initial point in the optimization process in the traditional SLAM algorithms, while the SLAM algorithms further improve the CNN prediction online. However, most of the current CNN-SLAM approaches have only taken advantage of the depth prediction but not yet other products from a CNN. In this work, we explore the use of the …
Spatial AI & Robotics (SAIR) Lab, Institute for Artificial Intelligence and Data Science, Department of Computer Science and Engineering, University at Buffalo（空间人工智能与机器人实验室，人工智能与数据科学研究所，计算机科学与工程系，布法罗大学）
University of Science and Technology of China（中国科学技术大学）
In recent years, visual SLAM has achieved great progress and development, but in complex scenes, especially rotating scenes, the error of mapping will increase significantly, and the slam system is easy to lose track. In this article, we propose an InterpolationSLAM framework, which is a visual SLAM framework based on ORB-SLAM2. InterpolationSLAM is robust in rotating scenes for Monocular and RGB-D configurations. By detecting the rotation and performing interpolation processing at the rotated position, pose of the system can be estimated more accurately at the rotated position, thereby improv…
HERO-SLAM: Hybrid Enhanced Robust Optimization of Neural SLAM
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；Baidu Research（百度研究院）
We study a semantic SLAM problem faced by a robot tasked with autonomous weeding under the corn canopy. The goal is to detect corn stalks and localize them in a global coordinate frame. This is a challenging setup for existing algorithms because there is very little space between the camera and the plants, and the camera motion is primarily restricted to be along the row. To overcome these challenges, we present a multi-camera system where a side camera (facing the plants) is used for detection whereas front and back cameras are used for motion estimation. Next, we show how semantic features i…
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）
The University of Manchester（曼彻斯特大学）；Intel Labs China（英特尔中国实验室）；Beihang University（北京航空航天大学）
Progress in the last decade has brought about significant improvements in the accuracy and speed of SLAM systems, broadening their mapping capabilities. Despite these advancements, long-term operation remains a major challenge, primarily due to the wide spectrum of perturbations robotic systems may encounter. Increasing the robustness of SLAM algorithms is an ongoing effort, however it usually addresses a specific perturbation. Generalisation of robustness across a large variety of challenging scenarios is not well-studied nor understood. This paper presents a systematic evaluation of the robu…
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）
A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather conditions which degrade their imagery and point cloud data. Radar, whose operating electromagnetic spectrum is less affected by environmental changes, is promising although its distinct sensing geometry and noise characteristics bring open challenges when being exploited for SLAM. % However, there are still open challenges since most existing visual and LiDAR …
In this paper, we develop a robust, efficient visual SLAM system that utilizes spatial inhibition of low threshold, baseline lines, and closed-loop keyframe features. Using ORB-SLAM2, our methods include stereo matching, frame tracking, local bundle adjustment, and line and point global bundle adjustment. In particular, we contribute re-projection in line with the baseline. Fusing lines in the system consume colossal time, and we reduce the time from distributing points to utilizing spatial suppression of feature points. In addition, low threshold key points can be more effective in dealing wi…
Despite the growing interest for autonomous environmental monitoring, effective SLAM realization in native habitats remains largely unsolved. In this paper, we fill this gap by presenting a novel online graph-based SLAM system for 2D LiDAR sensor in natural environments. By taking advantage of robust weighting scheme, sliding-windowed optimization, fast scan-matcher and parallel computing, our system not only delivers stable performance in cluttered surroudings but also meets real-time constraint. Simulated and experimental results confirm the feasibility and efficiency in the overall design o…
Recent interest towards autonomous navigation and exploration robots for indoor applications has spurred research into indoor Simultaneous Localization and Mapping (SLAM) robot systems. While most of these SLAM systems use Visual and LiDAR sensors in tandem with an odometry sensor, these odometry sensors drift over time. To combat this drift, Visual SLAM systems deploy compute and memory intensive search algorithms to detect `Loop Closures', which make the trajectory estimate globally consistent. To circumvent these resource (compute and memory) intensive algorithms, we present ViWiD, which in…
SKiD-SLAM: Robust, Lightweight, and Distributed Multi-Robot LiDAR SLAM in Resource-Constrained Field Environments
Dept. Electr. and Comput. Eng.（电子与计算机工程系）；Inha University（inha大学）；Laboratory for Information & Decision Systems (LIDS)（信息与决策系统实验室）；Massachusetts Institute of Technology（麻省理工学院）
RoDyn-SLAM: Robust Dynamic Dense RGB-D SLAM with Neural Radiance Fields
School of Data Science, Fudan University（复旦大学数据科学学院）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；ByteDance（字节跳动）
RoDyn-SLAM 提出一种基于神经辐射场的动态稠密 RGB-D SLAM 框架，通过融合光流与语义的运动掩码滤除动态干扰，并采用分而治之位姿优化与边缘扭曲损失，在准确性和鲁棒性上达到最先进水平。
This paper proposes a 3D LiDAR SLAM algorithm named Ground-SLAM, which exploits grounds in structured multi-floor environments to compress the pose drift mainly caused by LiDAR measurement bias. Ground-SLAM is developed based on the well-known pose graph optimization framework. In the front-end, motion estimation is conducted using LiDAR Odometry (LO) with a novel sensor-centric sliding map introduced, which is maintained by filtering out expired features based on the model of error propagation. At each key-frame, the sliding map is recorded as a local map. The ground nearby is extracted and m…
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…
In recent years, visual SLAM has achieved great progress and development in different scenes, however, there are still many problems to be solved. The SLAM system is not only restricted by the external scenes but is also affected by its movement mode, such as movement speed, rotational motion, etc. As the representatives of the most excellent networks for frame interpolation, Sepconv-slomo and EDSC can predict high-quality intermediate frame between the previous frame and the current frame. Intuitively, frame interpolation technology can enrich the information of images sequences, the number o…
We present Wildcat, a novel online 3D lidar-inertial SLAM system with exceptional versatility and robustness. At its core, Wildcat combines a robust real-time lidar-inertial odometry module, utilising a continuous-time trajectory representation, with an efficient pose-graph optimisation module that seamlessly supports both the single- and multi-agent settings. The robustness of Wildcat was recently demonstrated in the DARPA Subterranean Challenge where it outperformed other SLAM systems across various types of sensing-degraded and perceptually challenging environments. In this paper, we extens…
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…
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…
Simultaneous Localization And Mapping (SLAM) is a task to estimate the robot location and to reconstruct the environment based on observation from sensors such as LIght Detection And Ranging (LiDAR) and camera. It is widely used in robotic applications such as autonomous driving and drone delivery. Traditional LiDAR-based SLAM algorithms mainly leverage the geometric features from the scene context, while the intensity information from LiDAR is ignored. Some recent deep-learning-based SLAM algorithms consider intensity features and train the pose estimation network in an end-to-end manner. How…
Institute of Robotics and Mechatronics（机器人与机电一体化研究所）；German Aerospace Center (DLR)（德国航空航天中心（DLR））；Centre for Autonomous Systems（自主系统中心）；University of Technology Sydney（悉尼科技大学）
Simultaneous Localization and Mapping (SLAM) techniques play a key role towards long-term autonomy of mobile robots due to the ability to correct localization errors and produce consistent maps of an environment over time. Contrarily to urban or man-made environments, where the presence of unique objects and structures offer unique cues for localization, the appearance of unstructured natural environments is often ambiguous and self-similar, hindering the performances of loop closure detection. In this paper, we present an approach to improve the robustness of place recognition in the context …
Polytechnique Montréal（蒙特利尔综合理工学院）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）
To achieve collaborative tasks, robots in a team need to have a shared understanding of the environment and their location within it. Distributed Simultaneous Localization and Mapping (SLAM) offers a practical solution to localize the robots without relying on an external positioning system (e.g. GPS) and with minimal information exchange. Unfortunately, current distributed SLAM systems are vulnerable to perception outliers and therefore tend to use very conservative parameters for inter-robot place recognition. However, being too conservative comes at the cost of rejecting many valid loop clo…
Leveraging Semantic Graphs for Efficient and Robust LiDAR SLAM
College of Intelligence Science and Technology and the National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, China（智能科学与技术学院和装备状态感知与智能支撑国家重点实验室，国防科技大学，中国）；T Stone Robotics Institute and Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong, China（石墨机器人研究院和机械与自动化工程系，香港中文大学，中国）
In robot localisation and mapping, outliers are unavoidable when loop-closure measurements are taken into account. A single false-positive loop-closure can have a very negative impact on SLAM problems causing an inferior trajectory to be produced or even for the optimisation to fail entirely. To address this issue, popular existing approaches define a hard switch for each loop-closure constraint. This paper presents AEROS, a novel approach to adaptively solve a robust least-squares minimisation problem by adding just a single extra latent parameter. It can be used in the back-end component of …
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…
DAGS-SLAM: Dynamic-Aware 3DGS SLAM via Spatiotemporal Motion Probability and Uncertainty-Aware Scheduling
School of Mathematics, Hefei University of Technology（合肥工业大学数学学院）；School of Software, Tsinghua University（清华大学软件学院）；School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）
A Sensor-Aware Phenomenological Framework for Lidar Degradation Simulation and SLAM Robustness Evaluation
Turku Intelligent Embedded and Robotic Systems (TIERS) Lab , University of Turku（图尔库智能嵌入与机器人系统实验室，图尔库大学）；School of Information Science and Technology, Fudan University（信息科学与技术学院，复旦大学）
Tsinghua University（清华大学）；Beijing Institute of Control Engineering（北京控制工程研究所）；University of Maryland（马里兰大学）
The data loss caused by unreliable network seriously impacts the results of remote visual SLAM systems. From our experiment, a loss of less than 1 second of data can cause a visual SLAM algorithm to lose tracking. We present a novel buffering method, ORBBuf, to reduce the impact of data loss on remote visual SLAM systems. We model the buffering problem as an optimization problem by introducing a similarity metric between frames. To solve the buffering problem, we present an efficient greedy-like algorithm to discard the frames that have the least impact on the quality of SLAM results. We imple…
WaterSplat-SLAM: Photorealistic Monocular SLAM in Underwater Environment
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Key Laboratory of Marine Robotics, Shenyang（沈阳海洋机器人重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子及计算机工程学系）
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 …
Good Weights: Proactive, Adaptive Dead Reckoning Fusion for Continuous and Robust Visual SLAM
School of Electrical and Computer Engineering（电气与计算机工程学院）；Institute of Robotics and Intelligent Machines（机器人与智能机械研究所）；Georgia Institute of Technology（佐治亚理工学院）
TWC-SLAM: Multi-Agent Cooperative SLAM with Text Semantics and WiFi Features Integration for Similar Indoor Environments
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东人工智能与数字经济实验室）；Shenzhen University（深圳大学）；College of natural resources and environment（自然资源与环境学院）
