This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation. Each sensor is processed by a bio-inspired Spiking Neural Network (SNN) with continual Spike-Timing-Dependent Plasticity (STDP) learning, as observed in the brain. In contrast to most learning-based SLAM systems%, which a) require the acquisition of a representative dataset of the environment in which navigation must be performed and b) require an off-line training phase, our method does not require any offline training phase, but rather the SNN continuously learns features from the input data on the fly via STDP. At the same time, the SNN outputs are used as feature descriptors for loop closure detection and map correction. We conduct numerous experiments to benchmark our system against state-of-the-art RGB methods and we demonstrate the robustness of our DVS-Radar SLAM approach under strong lighting variations.
Most classical SLAM systems rely on the static scene assumption, which limits their applicability in real world scenarios. Recent SLAM frameworks have been proposed to simultaneously track the camera and moving objects. However they are often unable to estimate the canonical pose of the objects and exhibit a low object tracking accuracy. To solve this problem we propose TwistSLAM++, a semantic, dynamic, SLAM system that fuses stereo images and LiDAR information. Using semantic information, we track potentially moving objects and associate them to 3D object detections in LiDAR scans to obtain t…
Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳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））；Westlake University（西湖大学）；Westlake Institute for Advanced Study（西湖高等研究院）；The University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
Dynamic objects in the environment, such as people and other agents, lead to challenges for existing simultaneous localization and mapping (SLAM) approaches. To deal with dynamic environments, computer vision researchers usually apply some learning-based object detectors to remove these dynamic objects. However, these object detectors are computationally too expensive for mobile robot on-board processing. In practical applications, these objects output noisy sounds that can be effectively detected by on-board sound source localization. The directional information of the sound source object can…
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…
This paper presents Lidar-based Simultaneous Localization and Mapping (SLAM) for autonomous driving vehicles. Fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF) plus the observability of the system are investigated. In addition to the vehicle's states and landmark positions, a self-tuning filter estimates the IMU calibration parameters as well as the covariance of the measurement noise. The discrete-time covariance matrix of the process noise, the state transition matrix, and the observation sensitivity matrix are derived in clo…
Imperial College London（帝国理工学院）；Technical University of Munich（慕尼黑工业大学）
In this paper, we present a tightly-coupled visual-inertial object-level multi-instance dynamic SLAM system. Even in extremely dynamic scenes, it can robustly optimise for the camera pose, velocity, IMU biases and build a dense 3D reconstruction object-level map of the environment. Our system can robustly track and reconstruct the geometries of arbitrary objects, their semantics and motion by incrementally fusing associated colour, depth, semantic, and foreground object probabilities into each object model thanks to its robust sensor and object tracking. In addition, when an object is lost or …
Delivery robots aim to achieve high precision to facilitate complete autonomy. A precise three-dimensional point cloud map of sidewalk surroundings is required to estimate self-location. With or without the loop closing method, the cumulative error increases gradually after mapping for larger urban or city maps due to sensor drift. Therefore, there is a high risk of using the drifted or misaligned map. This article presented a technique for fusing GPS to update the 3D point cloud and eliminate cumulative error. The proposed method shows outstanding results in quantitative comparison and qualit…
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…
Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high speed motions or in scenes characterized by high dynamic range. However, event cameras output only little information when the amount of motion is limited, such as in the case of almost still motion. Conversely, standard cameras provide instant and rich information about the environment most of the time (in low-speed and…
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）
Increasing SLAM Pose Accuracy by Ground-to-Satellite Image Registration
Robotics Institute, University of Technology Sydney（悉尼科技大学机器人研究所）；ShanghaiTech University（上海科技大学）；College of Engineering and Computer Science, Australian National University（澳大利亚国立大学工程与计算机科学学院）；Data61, CSIRO（联邦科学与工业研究组织Data61）；Ford Motor Company（福特汽车公司）
A Survey on Collaborative SLAM with 3D Gaussian Splatting
Institute of System Integration, Le Quy Don Technical University, Hanoi, 10000, Vietnam（越南吕文技术大学系统整合研究所）；Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology（日本先进科学技术研究院先进科学技术研究生院）；University of Engineering and Technology, Vietnam National University, Hanoi, 10000, Vietnam（越南国家大学工程技术大学）
Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM
Department of Computer, Control, and Management Engineering "Antonio Ruberti", Sapienza University of Rome（计算机、控制与管理工程系（Antonio Ruberti）, 罗马萨皮恩扎大学）；University of Stuttgart（斯图加特大学）
Passage-Aware Structural Mapping for RGB-D Visual SLAM
Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科研究中心）；University of Luxembourg（卢森堡大学）；Faculty of Science, Technology, and Medicine at the University of Luxembourg（卢森堡大学科学、技术与医学学院）；Universidad Politécnica de Madrid（马德里理工大学）
In search and rescue missions, time is an important factor; fast navigation and quickly acquiring situation awareness might be matters of life and death. Hence, the use of robots in such scenarios has been restricted by the time needed to explore and build a map. One way to speed up exploration and mapping is to reason about unknown parts of the environment using prior information. While previous research on using external priors for robot mapping mainly focused on accurate maps or aerial images, such data are not always possible to get, especially indoor. We focus on emergency maps as priors …
University of Technology, Sydney（悉尼科技大学）；Centre for Autonomous Systems（自主系统中心）
In minimal invasive surgery, it is important to rebuild and visualize the latest deformed shape of soft-tissue surfaces to mitigate tissue damages. This paper proposes an innovative Simultaneous Localization and Mapping (SLAM) algorithm for deformable dense reconstruction of surfaces using a sequence of images from a stereoscope. We introduce a warping field based on the Embedded Deformation (ED) nodes with 3D shapes recovered from consecutive pairs of stereo images. The warping field is estimated by deforming the last updated model to the current live model. Our SLAM system can: (1) Increment…
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）
An Underwater, Fault-Tolerant, Laser-Aided Robotic Multi-Modal Dense SLAM System for Continuous Underwater In-Situ Observation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Keep It CALM: Toward Calibration-Free Kilometer-Level SLAM with Visual Geometry Foundation Models via an Assistant Eye
School of Automation and Intelligent Sensing, and Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai Jiao Tong University（自动化与智能感知学院，导航与位置服务上海市重点实验室，上海交通大学）；School of Electrical Engineering and Computer Science, The University of Queensland（电气工程与计算机科学学院，昆士兰大学）；School of Computer Science and Technology, Tongji University（计算机科学与技术学院，同济大学）
Massachusetts Institute of Technology（麻省理工学院）；University of Maryland（马里兰大学）；Amazon（亚马逊）；Northeastern University（东北大学）
We present the first distributed optimization algorithm with lazy communication for collaborative geometric estimation, the backbone of modern collaborative simultaneous localization and mapping (SLAM) and structure-from-motion (SfM) applications. Our method allows agents to cooperatively reconstruct a shared geometric model on a central server by fusing individual observations, but without the need to transmit potentially sensitive information about the agents themselves (such as their locations). Furthermore, to alleviate the burden of communication during iterative optimization, we design a…
Versatile LiDAR-Inertial Odometry With SE (2) Constraints for Ground Vehicles
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；KINDI Center for Computing Research, College of Engineering, Qatar University（卡塔尔大学工程学院金迪计算研究中心）
本文提出一种紧耦合 LiDAR-惯性 SLAM 框架，将地面车辆位姿参数化于 SE(2)，并把越界运动扰动纳入噪声模型，融合传感器与运动先验以提升室内外定位精度和鲁棒性。
Technische Universität Berlin（柏林工业大学）；Einstein Center Digital Future（爱因斯坦数字未来中心）；Science of Intelligence Excellence Cluster（智能科学卓越集群）
Event cameras are bio-inspired sensors that offer advantages over traditional cameras. They operate asynchronously, sampling the scene at microsecond resolution and producing a stream of brightness changes. This unconventional output has sparked novel computer vision methods to unlock the camera's potential. Here, the problem of event-based stereo 3D reconstruction for SLAM is considered. Most event-based stereo methods attempt to exploit the high temporal resolution of the camera and the simultaneity of events across cameras to establish matches and estimate depth. By contrast, this work inve…
Technische Universität Berlin（柏林工业大学）；Einstein Center Digital Future（爱因斯坦数字未来中心）
Event-based cameras are bio-inspired vision sensors whose pixels work independently from each other and respond asynchronously to brightness changes, with microsecond resolution. Their advantages make it possible to tackle challenging scenarios in robotics, such as high-speed and high dynamic range scenes. We present a solution to the problem of visual odometry from the data acquired by a stereo event-based camera rig. Our system follows a parallel tracking-and-mapping approach, where novel solutions to each subproblem (3D reconstruction and camera pose estimation) are developed with two objec…
