Large-scale construction and demolition significantly challenge long-term place recognition (PR) by drastically reshaping urban and suburban environments. Existing datasets predominantly reflect limited or indoor-focused changes, failing to adequately represent extensive outdoor transformations. To bridge this gap, we introduce the City that Never Settles (CNS) dataset, a simulation-based dataset created using the CARLA simulator, capturing major structural changes-such as building construction and demolition-across diverse maps and sequences. Additionally, we propose TCR_sym, a symmetric version of the original TCR metric, enabling consistent measurement of structural changes irrespective of source-target ordering. Quantitative comparisons demonstrate that CNS encompasses more extensive transformations than current real-world benchmarks. Evaluations of state-of-the-art LiDAR-based PR methods on CNS reveal substantial performance degradation, underscoring the need for robust algorithms capable of handling significant environmental changes. Our dataset is available at https://github.com/Hyunho111/CNS_dataset.
SLUM-i: Semi-supervised Learning for Urban Mapping of Informal Settlements and Data Quality Benchmarking
School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST)（电气工程与计算机科学学院，国立科学与技术大学（NUST））；Smart Data & Knowledge Services, German Research Center for Artificial Intelligence (DFKI)（智能数据与知识服务，德国人工智能研究中心（DFKI））
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；University of Jena（耶拿大学）
Detecting vehicles and representing their position and orientation in the three dimensional space is a key technology for autonomous driving. Recently, methods for 3D vehicle detection solely based on monocular RGB images gained popularity. In order to facilitate this task as well as to compare and drive state-of-the-art methods, several new datasets and benchmarks have been published. Ground truth annotations of vehicles are usually obtained using lidar point clouds, which often induces errors due to imperfect calibration or synchronization between both sensors. To this end, we propose Citysc…
Recent years have witnessed the increasing application of place recognition in various environments, such as city roads, large buildings, and a mix of indoor and outdoor places. This task, however, still remains challenging due to the limitations of different sensors and the changing appearance of environments. Current works only consider the use of individual sensors, or simply combine different sensors, ignoring the fact that the importance of different sensors varies as the environment changes. In this paper, an adaptive weighting visual-LiDAR fusion method, named AdaFusion, is proposed to …
A comparative assessment of global building and settlement datasets across geographic and settlement contexts
The World Bank, Global Facility for Disaster Reduction and Recovery（世界银行，全球减灾与恢复基金）；Center for Geospatial Analytics, Clark University（克拉克大学地理空间分析中心）；University of Twente（特文特大学）；School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）；School of Architecture and Planning, Morgan State University（摩根州立大学建筑与规划学院）
PRISM-Loc: a Lightweight Long-range LiDAR Localization in Urban Environments with Topological Maps
Federal Research Center for Computer Science and Control of Russian Academy of Sciences（俄罗斯科学院计算机科学与控制联邦研究中心）；Moscow Institute of Physics and Technology (MIPT)（莫斯科物理技术学院）；Artificial Intelligence Research Institute (AIRI)（人工智能研究所）；Innopolis University（Innopolis大学）
Boosting LiDAR-Based Localization with Semantic Insight: Camera Projection versus Direct LiDAR Segmentation
Department of Technical Cognitive Systems, FZI Research Center for Information Technology（技术认知系统部门，信息技术研究所以暨创新中心）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄大学）
University of Freiburg（弗莱堡大学）；Università degli studi di Milano - Bicocca（米兰比可卡大学）
Loop closure detection is an essential component of Simultaneous Localization and Mapping (SLAM) systems, which reduces the drift accumulated over time. Over the years, several deep learning approaches have been proposed to address this task, however their performance has been subpar compared to handcrafted techniques, especially while dealing with reverse loops. In this paper, we introduce the novel LCDNet that effectively detects loop closures in LiDAR point clouds by simultaneously identifying previously visited places and estimating the 6-DoF relative transformation between the current sca…
University of California, Berkeley（加州大学伯克利分校）；Stanford University（斯坦福大学）；University of Southern California（南加州大学）；Bytedance Research（字节跳动研究院）
We present HoliCity, a city-scale 3D dataset with rich structural information. Currently, this dataset has 6,300 real-world panoramas of resolution $13312 \times 6656$ that are accurately aligned with the CAD model of downtown London with an area of more than 20 km$^2$, in which the median reprojection error of the alignment of an average image is less than half a degree. This dataset aims to be an all-in-one data platform for research of learning abstracted high-level holistic 3D structures that can be derived from city CAD models, e.g., corners, lines, wireframes, planes, and cuboids, with t…
L2M-Reg: Building-level Uncertainty-aware Registration of Outdoor LiDAR Point Clouds and Semantic 3D City Models
Chair of Engineering Geodesy, TUM School of Engineering and Design, Technical University of Munich（工程测量学教授会，技术大学慕尼黑工程与设计学院）；Chair of Geoinformatics, TUM School of Engineering and Design, Technical University of Munich（地理信息学教授会，技术大学慕尼黑工程与设计学院）
We present a robust and precise localization system that achieves centimeter-level localization accuracy in disparate city scenes. Our system adaptively uses information from complementary sensors such as GNSS, LiDAR, and IMU to achieve high localization accuracy and resilience in challenging scenes, such as urban downtown, highways, and tunnels. Rather than relying only on LiDAR intensity or 3D geometry, we make innovative use of LiDAR intensity and altitude cues to significantly improve localization system accuracy and robustness. Our GNSS RTK module utilizes the help of the multi-sensor fus…
Carnegie Mellon University（卡内基梅隆大学）；University of California San Diego（加州大学圣地亚哥分校）
For long-term autonomy, most place recognition methods are mainly evaluated on simplified scenarios or simulated datasets, which cannot provide solid evidence to evaluate the readiness for current Simultaneous Localization and Mapping (SLAM). In this paper, we present a long-term place recognition dataset for use in mobile localization under large-scale dynamic environments. This dataset includes a campus-scale track and a city-scale track: 1) the campus-track focuses the long-term property, we record LiDAR device and an omnidirectional camera on 10 trajectories, and each trajectory are repeat…
GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Indian Institute of Tropical Meteorology（印度热带气象研究所）；CIEMAT（西班牙国家研究中心（能源、环境与技术研究中心））；Purdue University（普渡大学）；National Center for Atmospheric Research（美国国家大气研究中心）
Safe autonomous landing in urban cities is a necessity for the growing Unmanned Aircraft Systems (UAS) industry. In urgent situations, building rooftops, particularly flat rooftops, can provide local safe landing zones for small UAS. This paper investigates the real-time identification and selection of safe landing zones on rooftops based on LiDAR and camera sensor feedback. A visual high fidelity simulated city is constructed in the Unreal game engine, with particular attention paid to accurately generating rooftops and the common obstructions found thereon, e.g., ac units, water towers, air …
Department of Computer Science and Artificial Intelligence, University of Jeddah（计算机科学与人工智能系，朱德亚大学）；Department of Computer Science and Software Engineering, University of Western Australia（计算机科学与软件工程系，西澳大学）；Department of Data Science, Umm Al-Qura University（数据科学系，乌姆·阿勒·卡拉大学）；Department of Information and Technology Systems, College of Computer Science and Engineering, University of Jeddah（信息与技术系统系，计算机科学与工程学院，朱德亚大学）
Carnegie Mellon University（卡内基梅隆大学）；University of California San Diego（加州大学圣地亚哥分校）
We present BioSLAM, a lifelong SLAM framework for learning various new appearances incrementally and maintaining accurate place recognition for previously visited areas. Unlike humans, artificial neural networks suffer from catastrophic forgetting and may forget the previously visited areas when trained with new arrivals. For humans, researchers discover that there exists a memory replay mechanism in the brain to keep the neuron active for previous events. Inspired by this discovery, BioSLAM designs a gated generative replay to control the robot's learning behavior based on the feedback reward…
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 …
State Key Laboratory of Industrial Control Technology（工业控制技术国家重点实验室）；Institute of Cyber-Systems and Control, Zhejiang University（浙江大学网络空间安全与控制学院）
Place recognition is indispensable for a drift-free localization system. Due to the variations of the environment, place recognition using single-modality has limitations. In this paper, we propose a bi-modal place recognition method, which can extract a compound global descriptor from the two modalities, vision and LiDAR. Specifically, we first build the elevation image generated from 3D points as a structural representation. Then, we derive the correspondences between 3D points and image pixels that are further used in merging the pixel-wise visual features into the elevation map grids. In t…
University of Colorado Boulder（科罗拉多大学博尔德分校）；Cooperative Institute for Research in Environmental Sciences (CIRES)（环境科学合作研究所（CIRES））；Institute of Behavioral Science（行为科学研究所）
Despite abundant data on the spatial distribution of contemporary human settlements, historical data on the long-term evolution of human settlements at fine spatial and temporal granularity is scarce, limiting our quantitative understanding of long-term changes of built-up areas. This is because commonly used mapping methods (e.g., image classification) and suitable data sources (i.e., aerial imagery, multi-spectral remote sensing data, LiDAR) have only been available in recent decades. However, there are alternative data sources such as cadastral records that are digitally available, containi…
The final project report for the SmartShuttle sub-project of the Ohio State University is presented in this report. This has been a two year project where the unified, scalable and replicable automated driving architecture introduced by the Automated Driving Lab of the Ohio State University has been further developed, replicated in different vehicles and scaled between different vehicle sizes. A limited scale demonstration was also conducted during the first year of the project. The architecture used was further developed in the second project year including parameter space based low level con…
MINES ParisTech, PSL University, Centre for Robotics（巴黎高科矿业学院，巴黎文理研究大学，机器人中心）
KITTI-CARLA is a dataset built from the CARLA v0.9.10 simulator using a vehicle with sensors identical to the KITTI dataset. The vehicle thus has a Velodyne HDL64 LiDAR positioned in the middle of the roof and two color cameras similar to Point Grey Flea 2. The positions of the LiDAR and cameras are the same as the setup used in KITTI. The objective of this dataset is to test approaches of semantic segmentation LiDAR and/or images, odometry LiDAR and/or image in synthetic data and to compare with the results obtained on real data like KITTI. This dataset thus makes it possible to improve trans…
Ford AV LLC（福特自动驾驶有限责任公司）；Ford Motor Company（福特汽车公司）
This paper presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18. The vehicles traversed an average route of 66 km in Michigan that included a mix of driving scenarios such as the Detroit Airport, freeways, city-centers, university campus and suburban neighbourhoods, etc. Each vehicle used in this data collection is a Ford Fusion outfitted with an Applanix POS-LV GNSS system, four HDL-32E Velodyne 3D-lidar scanners, 6 Point Grey 1.3 MP Cameras arranged on the rooftop for 360-degree coverage and 1 Pointgre…
