Map construction in large scale outdoor environment is of importance for robots to robustly fulfill their tasks. Massive sessions of data should be merged to distinguish low dynamics in the map, which otherwise might debase the performance of localization and navigation algorithms. In this paper we propose a method for multi-session map construction in large scale outdoor environment using 3D LiDAR. To efficiently align the maps from different sessions, a laser-based loop closure detection method is integrated and the sequential information within the submaps is utilized for higher robustness. Furthermore, a dynamic detection method is proposed to detect dynamics in the overlapping areas among sessions of maps. We test the method in the real-world environment with a VLP-16 Velodyne LiDAR and the experimental results prove the validity and robustness of the proposed method.
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 …
Multi-Mapcher: Loop Closure Detection-Free Heterogeneous LiDAR Multi-Session SLAM Leveraging Outlier-Robust Registration for Autonomous Vehicles
Laboratory for Information & Decision Systems, Massachusetts Institute of Technology（信息与决策系统实验室，麻省理工学院）；School of Electrical Engineering, KAIST（电气工程学院，韩国科学技术院）
We present a novel method for reliable robot navigation in uneven outdoor terrains. Our approach employs a novel fully-trained Deep Reinforcement Learning (DRL) network that uses elevation maps of the environment, robot pose, and goal as inputs to compute an attention mask of the environment. The attention mask is used to identify reduced stability regions in the elevation map and is computed using channel and spatial attention modules and a novel reward function. We continuously compute and update a navigation cost-map that encodes the elevation information or the level-of-flatness of the ter…
Interdisciplinary Institute for Technological Innovation (3IT)（跨学科技术创新研究所（3IT））；Université de Sherbrooke（舍布鲁克大学）
For long-term simultaneous planning, localization and mapping (SPLAM), a robot should be able to continuously update its map according to the dynamic changes of the environment and the new areas explored. With limited onboard computation capabilities, a robot should also be able to limit the size of the map used for online localization and mapping. This paper addresses these challenges using a memory management mechanism, which identifies locations that should remain in a Working Memory (WM) for online processing from locations that should be transferred to a Long-Term Memory (LTM). When revis…
ETH Zürich（苏黎世联邦理工学院）；Sevensense Robotics AG（七感机器人股份公司）
We introduce a novel method for oriented place recognition with 3D LiDAR scans. A Convolutional Neural Network is trained to extract compact descriptors from single 3D LiDAR scans. These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for bootstrapping local registration methods. We employ a triplet loss function for training and use a hard-negative mining strategy to further increase the performance of our descriptor extractor. In an evaluation on the NCLT and KITTI datasets, we demonstrate that our method outperforms related state-…
Commonly, SLAM algorithms are focused on a static environment, however, there are several scenes where dynamic objects are present. This work presents the STDyn-SLAM an image feature-based SLAM system working on dynamic environments using a series of sub-systems, like optic flow, orb features extraction, visual odometry, and convolutional neural networks to discern moving objects in the scene. The neural network is used to support object detection and segmentation to avoid erroneous maps and wrong system localization. The STDyn-SLAM employs a stereo pair and is developed for outdoor environmen…
Dahlem Center for Machine Learning and Robotics (DCMLR), Department of Mathematics and Computer Science, Freie Universität Berlin（达赫姆机器学习与机器人研究中心（DCMLR），数学与计算机科学系，柏林自由大学）
Interdisciplinary Institute of Technological Innovation (3IT)（跨学科技术创新研究所（3IT））
For robots navigating using only a camera, illumination changes in indoor environments can cause re-localization failures during autonomous navigation. In this paper, we present a multi-session visual SLAM approach to create a map made of multiple variations of the same locations in different illumination conditions. The multi-session map can then be used at any hour of the day for improved re-localization capability. The approach presented is independent of the visual features used, and this is demonstrated by comparing re-localization performance between multi-session maps created using the …
This paper addresses outdoor terrain mapping using overhead images obtained from an unmanned aerial vehicle. Dense depth estimation from aerial images during flight is challenging. While feature-based localization and mapping techniques can deliver real-time odometry and sparse points reconstruction, a dense environment model is generally recovered offline with significant computation and storage. This paper develops a joint 2D-3D learning approach to reconstruct local meshes at each camera keyframe, which can be assembled into a global environment model. Each local mesh is initialized from sp…
Faculty of Electronic and Information Engineering, Xi’an Jiaotong University（西安交通大学电子与信息工程学院）；College of Artificial Intelligence, Xi’an Jiaotong University（西安交通大学人工智能学院）
Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction. In this paper, we built a system to creat a semantic 3D map by combining 3D point cloud from ORB SLAM with semantic segmentation information from Convolutional Neural Network model PSPNet-101 for large-scale environments. Besides, a new dataset for KITTI sequences has been built, which contains the GPS information and labels of landmarks from Google Map i…
Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments
University College London（伦敦大学学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；Frontiers Science Center for Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统前沿科学中心）
G-DRAGON: Geospatial Reasoning and Dynamic Planning for Retrieval-Augmented Outdoor Navigation
Spatial AI & Robotics Lab（空间人工智能与机器人实验室）；University at Buffalo（布法罗大学）；School of Future Technology（未来技术学院）；Shanghai University（上海大学）；University of Chinese Academy of Sciences（中国科学院大学）
We propose a novel method for autonomous legged robot navigation in densely vegetated environments with a variety of pliable/traversable and non-pliable/untraversable vegetation. We present a novel few-shot learning classifier that can be trained on a few hundred RGB images to differentiate flora that can be navigated through, from the ones that must be circumvented. Using the vegetation classification and 2D lidar scans, our method constructs a vegetation-aware traversability cost map that accurately represents the pliable and non-pliable obstacles with lower, and higher traversability costs,…
Recently, there have been numerous advances in the development of biologically inspired lightweight Micro Aerial Vehicles (MAVs). While autonomous navigation is fairly straight-forward for large UAVs as expensive sensors and monitoring devices can be employed, robust methods for obstacle avoidance remains a challenging task for MAVs which operate at low altitude in cluttered unstructured environments. Due to payload and power constraints, it is necessary for such systems to have autonomous navigation and flight capabilities using mostly passive sensors such as cameras. In this paper, we descri…
Scalable Outdoors Autonomous Drone Flight with Visual-Inertial SLAM and Dense Submaps Built without LiDAR
Technical University of Munich（技术大学慕尼黑）；School of Computation, Information and Technology（计算、信息与技术学院）；Mobile Robotics Lab（移动机器人实验室）；Department of Computing（计算学院）；Imperial College London（伦敦帝国学院）；Department of Mechanical and Process Engineering（机械与过程工程学院）；ETH Zurich（苏黎世联邦理工学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
CoMo3R-SLAM: Collaborative Monocular Dense SLAM with Learned 3D Reconstruction Priors for Outdoor Multi-Agent Systems
ETH Zurich（苏黎世联邦理工学院）；University of Liverpool（利物浦大学）；Harbin Engineering University（哈尔滨工程大学）；University of Ottawa（Ottawa大学）；Shanghai Jiao Tong University（上海交通大学）；Imperial College London（伦敦帝国理工学院）
OpenNavMap: Multi-Session Appearance-Based Topometric Mapping for Scalable Visual Navigation
Robot Perception and Learning Lab, Intelligent Robotics, Department of Computer Science, University College London（机器人感知与学习实验室，智能机器人，计算机科学系，伦敦大学学院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（计算机科学与工程系，香港科学与技术大学）；Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；Institute of Robotics and Automatic Information System, Nankai University（机器人与自动信息系统研究所，南开大学）；College of control science and engineering, Zhejiang University（控制科学与工程学院，浙江大学）；AI Centre, Department of Computer Science, University College London（人工智能中心，计算机科学系，伦敦大学学院）
ETH Zürich（苏黎世联邦理工学院）；Sevensense Robotics AG（Sevensense机器人公司）
Changes in appearance is one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present VIZARD, a visual localization system for urban outdoor environments. By combining a local localization algorithm with the use of multi-session maps, a high localization recall can be achieved across vastly different appearance conditions. The fusion of the visual localization constraints with wheel-odometry in a state estimation framework further guarantees smooth and accurate pose estimates. In an extensive experimental evaluation on several…
ETH Zürich（苏黎世联邦理工学院）；Sevensense Robotics AG（Sevensense机器人公司）
Visually poor scenarios are one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present MOZARD, a multi-modal localization system for urban outdoor environments using vision and LiDAR. By extending our preexisting key-point based visual multi-session local localization approach with the use of semantic data, an improved localization recall can be achieved across vastly different appearance conditions. In particular we focus on the use of curbstone information because of their broad distribution and reliability within urban en…
Rad-GS: Radar-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments
Department of Automation, School of Mechanical Engineering and Automation, Harbin Institute of Technology（自动化系、机械工程与自动化学院、哈尔滨工业大学）；School of Computer Science and Technology, Harbin Institute of Technology（计算机科学与技术学院、哈尔滨工业大学）
We present a complete map management process for a visual localization system designed for multi-vehicle long- term operations in resource constrained outdoor environments. Outdoor visual localization generates large amounts of data that need to be incorporated into a lifelong visual map in order to allow localization at all times and under all appearance conditions. Processing these large quantities of data is non- trivial, as it is subject to limited computational and storage capabilities both on the vehicle and on the mapping backend. We address this problem with a two-fold map update parad…
Floorplan-SLAM: A Real-Time, High-Accuracy, and Long-Term Multi-Session Point-Plane SLAM for Efficient Floorplan Reconstruction
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；College of Information Science and Technology, Beijing University of Chemical Technology（北京化工大学信息科学与技术学院）
