Auswahl der wissenschaftlichen Literatur zum Thema „Slam LiDAR“
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Zeitschriftenartikel zum Thema "Slam LiDAR"
Jie, Lu, Zhi Jin, Jinping Wang, Letian Zhang und Xiaojun Tan. „A SLAM System with Direct Velocity Estimation for Mechanical and Solid-State LiDARs“. Remote Sensing 14, Nr. 7 (04.04.2022): 1741. http://dx.doi.org/10.3390/rs14071741.
Der volle Inhalt der QuelleSier, Ha, Qingqing Li, Xianjia Yu, Jorge Peña Queralta, Zhuo Zou und Tomi Westerlund. „A Benchmark for Multi-Modal LiDAR SLAM with Ground Truth in GNSS-Denied Environments“. Remote Sensing 15, Nr. 13 (28.06.2023): 3314. http://dx.doi.org/10.3390/rs15133314.
Der volle Inhalt der QuelleZhao, Yu-Lin, Yi-Tian Hong und Han-Pang Huang. „Comprehensive Performance Evaluation between Visual SLAM and LiDAR SLAM for Mobile Robots: Theories and Experiments“. Applied Sciences 14, Nr. 9 (06.05.2024): 3945. http://dx.doi.org/10.3390/app14093945.
Der volle Inhalt der QuelleChen, Shoubin, Baoding Zhou, Changhui Jiang, Weixing Xue und Qingquan Li. „A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization“. Remote Sensing 13, Nr. 14 (10.07.2021): 2720. http://dx.doi.org/10.3390/rs13142720.
Der volle Inhalt der QuellePeng, Gang, Yicheng Zhou, Lu Hu, Li Xiao, Zhigang Sun, Zhangang Wu und Xukang Zhu. „VILO SLAM: Tightly Coupled Binocular Vision–Inertia SLAM Combined with LiDAR“. Sensors 23, Nr. 10 (09.05.2023): 4588. http://dx.doi.org/10.3390/s23104588.
Der volle Inhalt der QuelleDang, Xiangwei, Zheng Rong und Xingdong Liang. „Sensor Fusion-Based Approach to Eliminating Moving Objects for SLAM in Dynamic Environments“. Sensors 21, Nr. 1 (01.01.2021): 230. http://dx.doi.org/10.3390/s21010230.
Der volle Inhalt der QuelleDebeunne, César, und Damien Vivet. „A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping“. Sensors 20, Nr. 7 (07.04.2020): 2068. http://dx.doi.org/10.3390/s20072068.
Der volle Inhalt der QuelleXu, Xiaobin, Lei Zhang, Jian Yang, Chenfei Cao, Wen Wang, Yingying Ran, Zhiying Tan und Minzhou Luo. „A Review of Multi-Sensor Fusion SLAM Systems Based on 3D LIDAR“. Remote Sensing 14, Nr. 12 (13.06.2022): 2835. http://dx.doi.org/10.3390/rs14122835.
Der volle Inhalt der QuelleBu, Zean, Changku Sun und Peng Wang. „Semantic Lidar-Inertial SLAM for Dynamic Scenes“. Applied Sciences 12, Nr. 20 (18.10.2022): 10497. http://dx.doi.org/10.3390/app122010497.
Der volle Inhalt der QuelleAbdelhafid, El Farnane, Youssefi My Abdelkader, Mouhsen Ahmed, Dakir Rachid und El Ihyaoui Abdelilah. „Visual and light detection and ranging-based simultaneous localization and mapping for self-driving cars“. International Journal of Electrical and Computer Engineering (IJECE) 12, Nr. 6 (01.12.2022): 6284. http://dx.doi.org/10.11591/ijece.v12i6.pp6284-6292.
Der volle Inhalt der QuelleDissertationen zum Thema "Slam LiDAR"
Nava, Chocron Yoshua. „Visual-LiDAR SLAM with loop closure“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265532.
Der volle Inhalt der QuelleSpjutspetsen inom Lidar-baserade teknik för fordonsodometri har den senaste tiden uppnått exceptionella nivåer av noggrannhet. Med det sagt har de metoder som presenterats fokuserat på att lösa lokaliseringsproblemet och därför gjort förenklande antaganden såsom att de sköter kartläggning av miljön löpande utan platsåterkoppling, och att de inte kan återlokalisera i tidigare kända miljöer. Således utvecklar vi i detta arbete ett system som kombinerar dessa noggranna lidarodometriska tekniker med algoritmer för platsigenkänning för att möjliggöra loopdetektion. Vi använder vitt tillgängliga dataset av körning i stadstrafik samt i utomhusområden för utveckling och utvärdering av systemet. Resultaten visar att platsåterkoppling förbättrar noggrannheten hos Lidar-baserade lokaliseringsmetoder och gör dem mer robusta, samt att man med hjälp av detektorer baserade på punktmolnssegmentering och visuella särdrag erhåller ett system som uppvisar mycket goda resultat under utvärderingsfasen.
Contreras, Samamé Luis Federico. „SLAM collaboratif dans des environnements extérieurs“. Thesis, Ecole centrale de Nantes, 2019. http://www.theses.fr/2019ECDN0012/document.
Der volle Inhalt der QuelleThis thesis proposes large-scale mapping model of urban and rural environments using 3D data acquired by several robots. The work contributes in two main ways to the research field of mapping. The first contribution is the creation of a new framework, CoMapping, which allows to generate 3D maps in a cooperative way. This framework applies to outdoor environments with a decentralized approach. The CoMapping's functionality includes the following elements: First of all, each robot builds a map of its environment in point cloud format.To do this, the mapping system was set up on computers dedicated to each vehicle, processing distance measurements from a 3D LiDAR moving in six degrees of freedom (6-DOF). Then, the robots share their local maps and merge the point clouds individually to improve their local map estimation. The second key contribution is the group of metrics that allow to analyze the merging and card sharing processes between robots. We present experimental results to validate the CoMapping framework with their respective metrics. All tests were carried out in urban outdoor environments on the surrounding campus of the École Centrale de Nantes as well as in rural areas
Dellenbach, Pierre. „Exploring LiDAR Odometries through Classical, Deep and Inertial perspectives“. Electronic Thesis or Diss., Université Paris sciences et lettres, 2023. http://www.theses.fr/2023UPSLM069.
Der volle Inhalt der Quelle3D LiDARs have become increasingly popular in the past decade, notably motivated by the safety requirements of autonomous driving requiring new sensor modalities. Contrary to cameras, 3D LiDARs provide direct, and extremely precise 3D measurements of the environment. This has led to the development of many different mapping and Simultaneous Localization And Mapping (SLAM) solutions leveraging this new modality. These algorithms quickly performed much better than their camera-based counterparts, as evidenced by several open-source benchmarks. One critical component ofthese systems is LiDAR odometry. A LiDAR odometry is an algorithm estimating the trajectory of the sensor, given only the iterative integration of the LiDAR measurements. The focus of this work is on the topic of LiDAR Odometries. More precisely, we aim to push the boundaries of LiDAR odometries, both in terms of precision and performance.To achieve this, we first explore classical LiDAR odometries in depth, and propose two novel LiDAR odometries, in chapter 3. We show the strength, and limitations of such methods. Then, to address to improve them we first investigate Deep Learning for LiDAR odometries in chapter 4, notably focusing on end-to-end odometries. We show again the limitations of such approaches and finally investigate in chapter 5 fusing inertial and LiDAR measurements
Bruns, Christian. „Lidar-based Vehicle Localization in an Autonomous Valet Parking Scenario“. The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461236677.
Der volle Inhalt der QuelleEkström, Joakim. „3D Imaging Using Photon Counting Lidar on a Moving Platform“. Thesis, Linköpings universitet, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153297.
Der volle Inhalt der QuelleZhang, Erik. „Integration of IMU and Velodyne LiDAR sensor in an ICP-SLAM framework“. Thesis, KTH, Optimeringslära och systemteori, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193653.
Der volle Inhalt der QuelleLokalisering och kartläggning (SLAM) i en okänd miljö är ett viktigt steg för många autonoma system. Den föreslagna lösningen är inte beroende på att hitta nyckelpunkter eller nyckelobjekt. Till skillnad från många andra SLAM baserade metoder så arbetar denna metod med glesa punktmoln där 'generalized ICP' (GICP)algoritmen används för punktmolns registrering. I denna uppsats så föreslås en variant av GICP och undersöker, ifall en tröghetssensor (IMU) kan hjälpa till med SLAM-processen. LiDAR-data som har använts i denna uppsats har varit uppmätta från en Velodyne LiDAR monterat på en ryggsäck, en bil och på en UAV. Resultatet tyder på att IMU-data kan göra algoritmen robustare och från mätningar i stadsmiljö så visar det sig att IMU kan hjälpa till att minska vinkeldrift, vilket är det största felkällan för noggrannhet i det globala koordinat systemet.
Gonzalez, Cadenillas Clayder Alejandro. „An improved feature extractor for the lidar odometry and mapping algorithm“. Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/171499.
Der volle Inhalt der QuelleLa extracción de características es una tarea crítica en la localización y mapeo simultáneo o Simultaneous Localization and Mapping (SLAM) basado en características, que es uno de los problemas más importantes de la comunidad robótica. Un algoritmo que resuelve SLAM utilizando características basadas en LiDAR es el algoritmo LiDAR Odometry and Mapping (LOAM). Este algoritmo se considera actualmente como el mejor algoritmo SLAM según el Benchmark KITTI. El algoritmo LOAM resuelve el problema de SLAM a través de un enfoque de emparejamiento de características y su algoritmo de extracción de características detecta las características clasifican los puntos de una nube de puntos como planos o agudos. Esta clasificación resulta de una ecuación que define el nivel de suavidad para cada punto. Sin embargo, esta ecuación no considera el ruido de rango del sensor. Por lo tanto, si el ruido de rango del LiDAR es alto, el extractor de características de LOAM podría confundir los puntos planos y agudos, lo que provocaría que la tarea de emparejamiento de características falle. Esta tesis propone el reemplazo del algoritmo de extracción de características del LOAM original por el algoritmo Curvature Scale Space (CSS). La elección de este algoritmo se realizó después de estudiar varios extractores de características en la literatura. El algoritmo CSS puede mejorar potencialmente la tarea de extracción de características en entornos ruidosos debido a sus diversos niveles de suavizado Gaussiano. La sustitución del extractor de características original de LOAM por el algoritmo CSS se logró mediante la adaptación del algoritmo CSS al Velodyne VLP-16 3D LiDAR. El extractor de características de LOAM y el extractor de características de CSS se probaron y compararon con datos reales y simulados, incluido el dataset KITTI utilizando las métricas Optimal Sub-Pattern Assignment (OSPA) y Absolute Trajectory Error (ATE). Para todos estos datasets, el rendimiento de extracción de características de CSS fue mejor que el del algoritmo LOAM en términos de métricas OSPA y ATE.
Paiva, mendes Ellon. „Study on the Use of Vision and Laser Range Sensors with Graphical Models for the SLAM Problem“. Thesis, Toulouse, INSA, 2017. http://www.theses.fr/2017ISAT0016/document.
Der volle Inhalt der QuelleA strong requirement to deploy autonomous mobile robots is their capacity to localize themselves with a certain precision in relation to their environment. Localization exploits data gathered by sensors that either observe the inner states of the robot, like acceleration and speed, or the environment, like cameras and Light Detection And Ranging (LIDAR) sensors. The use of environment sensors has triggered the development of localization solutions that jointly estimate the robot position and the position of elements in the environment, referred to as Simultaneous Localization and Mapping (SLAM) approaches. To handle the noise inherent of the data coming from the sensors, SLAM solutions are implemented in a probabilistic framework. First developments were based on Extended Kalman Filters, while a more recent developments use probabilistic graphical models to model the estimation problem and solve it through optimization. This thesis exploits the latter approach to develop two distinct techniques for autonomous ground vehicles: oneusing monocular vision, the other one using LIDAR. The lack of depth information in camera images has fostered the use of specific landmark parametrizations that isolate the unknown depth in one variable, concentrating its large uncertainty into a single parameter. One of these parametrizations, named Parallax Angle Parametrization, was originally introduced in the context of the Bundle Adjustment problem, that processes all the gathered data in a single global optimization step. We present how to exploit this parametrization in an incremental graph-based SLAM approach in which robot motion measures are also incorporated. LIDAR sensors can be used to build odometry-like solutions for localization by sequentially registering the point clouds acquired along a robot trajectory. We define a graphical model layer on top of a LIDAR odometry layer, that uses the Iterative Closest Points (ICP) algorithm as registration technique. Reference frames are defined along the robot trajectory, and ICP results are used to build a pose graph, used to solve an optimization problem that enables the correction of the robot trajectory and the environment map upon loop closures. After an introduction to the theory of graphical models applied to SLAM problem, the manuscript depicts these two approaches. Simulated and experimental results illustrate the developments throughout the manuscript, using classic and in-house datasets
Chghaf, Mohammed. „Towards a Multimodal Loop Closure System for Real-Time Embedded SLAM Applications“. Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPAST133.
Der volle Inhalt der QuelleMultimodal SLAM algorithms improve its robustness and accuracy in complex and dynamic environments. However, these improvements come at the cost of increased computational requirements. The systemic-level study of the SLAM problem is crucial to designing a practical, stable and versatile solution, adaptable to embedded and real-time systems. We have studied the various processing stages of the system in order to propose contributions to the multimodal loop closure level for SLAM applications, and its computational architecture. This study began with an in-depth analysis of the impact of multimodal information representation on loop closure accuracy and its influence on trajectory drift reduction. We developed a fusion method based on a similarity-guided particle filter, which was evaluated using various dataset. The results obtained showed an improvement in localization’s accuracy. We proposed a heterogeneous architecture model (CPU-GPU and CPU-FPGA) for inter-modal scene descriptor computation. This architecture was able to deliver superior performance in terms of processing time
Karlsson, Oskar. „Lidar-based SLAM : Investigation of environmental changes and use of road-edges for improved positioning“. Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165288.
Der volle Inhalt der QuelleBuchteile zum Thema "Slam LiDAR"
Zhang, Jiabao, und Yu Zhang. „Downsampling Assessment for LiDAR SLAM“. In Proceedings of 2023 Chinese Intelligent Automation Conference, 234–42. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6187-0_23.
Der volle Inhalt der QuellePeng, Gang, Tin Lun Lam, Chunxu Hu, Yu Yao, Jintao Liu und Fan Yang. „LiDAR SLAM for Mobile Robot“. In Introduction to Intelligent Robot System Design, 191–223. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1814-0_4.
Der volle Inhalt der QuelleZhao, Chunhui, Jiaxing Li, Anqi Chen, Yang Lyu und Lin Hua. „Intensity Augmented Solid-State-LiDAR-Inertial SLAM“. In Lecture Notes in Electrical Engineering, 129–39. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1103-1_12.
Der volle Inhalt der QuelleAndert, Franz, und Henning Mosebach. „LiDAR SLAM Positioning Quality Evaluation in Urban Road Traffic“. In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 277–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38822-5_19.
Der volle Inhalt der QuelleCho, Kuk, SeungHo Baeg und Sangdeok Park. „Natural Terrain Detection and SLAM Using LIDAR for UGV“. In Advances in Intelligent Systems and Computing, 793–805. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33926-4_76.
Der volle Inhalt der QuelleWang, Yuhang, und Liwei Zhang. „Lidar-Inertial SLAM Method for Accurate and Robust Mapping“. In Communications in Computer and Information Science, 33–44. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8018-5_3.
Der volle Inhalt der QuelleShi, Zhanhong, Ping Wang, Wanquan Liu und Chenqiang Gao. „Multi-Sensor SLAM Assisted by 2D LiDAR Line Features“. In Learning and Analytics in Intelligent Systems, 73–80. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56521-2_7.
Der volle Inhalt der QuelleChen, Yuwei, Jian Tang, Ziyi Feng, Teemu Hakala, Juha Hyyppä, Chuncheng Zhou, Lingli Tang und Chuanrong Li. „Possibility of Applying SLAM-Aided LiDAR in Deep Space Exploration“. In Springer Proceedings in Physics, 239–48. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49184-4_24.
Der volle Inhalt der QuelleCho, Kuk, SeungHo Baeg und Sangdeok Park. „Natural Terrain Detection and SLAM Using LIDAR for an UGV“. In Frontiers of Intelligent Autonomous Systems, 263–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35485-4_22.
Der volle Inhalt der QuelleChen, Chunxu, Ling Pei, Changqing Xu, Danping Zou, Yuhui Qi, Yifan Zhu und Tao Li. „Trajectory Optimization of LiDAR SLAM Based on Local Pose Graph“. In Lecture Notes in Electrical Engineering, 360–70. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7751-8_36.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Slam LiDAR"
Kim, Giseop, Seungsang Yun, Jeongyun Kim und Ayoung Kim. „SC-LiDAR-SLAM: A Front-end Agnostic Versatile LiDAR SLAM System“. In 2022 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2022. http://dx.doi.org/10.1109/iceic54506.2022.9748644.
Der volle Inhalt der QuelleAbati, Gabriel F., João Carlos V. Soares und Marco Antonio Meggiolaro. „SLAM Visual Em Ambientes Dinâmicos Usando Segmentação Panóptica“. In Anais Estendidos do Simpósio Brasileiro de Robótica e Simpósio Latino-Americano de Robótica. Sociedade Brasileira de Computação, 2023. http://dx.doi.org/10.5753/sbrlars_estendido.2023.235116.
Der volle Inhalt der Quellexu, bo, Yiran Fu, Changsai Zhang und Zhengjun Liu. „Research of cartographer laser SLAM algorithm“. In LIDAR Imaging Detection and Target Recognition 2017, herausgegeben von Yueguang Lv, Jianzhong Su, Wei Gong, Jian Yang, Weimin Bao, Weibiao Chen, Zelin Shi, Jindong Fei, Shensheng Han und Weiqi Jin. SPIE, 2017. http://dx.doi.org/10.1117/12.2292864.
Der volle Inhalt der QuelleLi, Jun, Junqiao Zhao, Yuchen Kang, Xudong He, Chen Ye und Lu Sun. „DL-SLAM: Direct 2.5D LiDAR SLAM for Autonomous Driving“. In 2019 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2019. http://dx.doi.org/10.1109/ivs.2019.8813868.
Der volle Inhalt der QuelleChang, Yu-Cheng, Ya-Li Chen, Ya-Wen Hsu, Jau-Woei Perng und Jun-Dong Chang. „Integrating V-SLAM and LiDAR-based SLAM for Map Updating“. In 2021 IEEE 4th International Conference on Knowledge Innovation and Invention (ICKII). IEEE, 2021. http://dx.doi.org/10.1109/ickii51822.2021.9574718.
Der volle Inhalt der QuelleFrosi, Matteo, und Matteo Matteucci. „D3VIL-SLAM: 3D Visual Inertial LiDAR SLAM for Outdoor Environments“. In 2023 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2023. http://dx.doi.org/10.1109/iv55152.2023.10186534.
Der volle Inhalt der QuelleGeneva, Patrick, Kevin Eckenhoff, Yulin Yang und Guoquan Huang. „LIPS: LiDAR-Inertial 3D Plane SLAM“. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8594463.
Der volle Inhalt der QuelleChen, Xieyuanli, Andres Milioto, Emanuele Palazzolo, Philippe Giguere, Jens Behley und Cyrill Stachniss. „SuMa++: Efficient LiDAR-based Semantic SLAM“. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8967704.
Der volle Inhalt der QuelleKirnos, Vasilii, Vladimir Antipov, Andrey Priorov und Vera Kokovkina. „The LIDAR Odometry in the SLAM“. In 2018 23rd Conference of Open Innovations Association (FRUCT). IEEE, 2018. http://dx.doi.org/10.23919/fruct.2018.8588026.
Der volle Inhalt der QuelleZhang, Xuanliang, und Wenguang Wang. „Moving target removal for lidar SLAM“. In Seventh Symposium on Novel Photoelectronic Detection Technology and Application 2020, herausgegeben von Junhao Chu, Qifeng Yu, Huilin Jiang und Junhong Su. SPIE, 2021. http://dx.doi.org/10.1117/12.2587440.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Slam LiDAR"
Ennasr, Osama, Michael Paquette und Garry Glaspell. UGV SLAM payload for low-visibility environments. Engineer Research and Development Center (U.S.), September 2023. http://dx.doi.org/10.21079/11681/47589.
Der volle Inhalt der QuelleChristie, Benjamin, Osama Ennasr und Garry Glaspell. Autonomous navigation and mapping in a simulated environment. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42006.
Der volle Inhalt der QuelleLee, W. S., Victor Alchanatis und Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, Januar 2014. http://dx.doi.org/10.32747/2014.7598158.bard.
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