Semi-supervised Semantic Mapping Through Label Propagation with Semantic Texture Meshes
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-019-01187-z
Abstract: Scene understanding is an important capability for robots acting in unstructured environments. While most SLAM approaches provide a geometrical representation of the scene, a semantic map is necessary for more complex interactions with the surroundings.… read more here.
Keywords: semantic texture; semantic map; scene; geometry ... See more keywords
Sparse semantic map building and relocalization for UGV using 3D point clouds in outdoor environments
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.02.103
Abstract: Abstract In this paper, we proposed a sparse semantic map building method and an outdoor relocalization strategy based on this map. Most existing semantic mapping approaches focus on improving semantic understanding of single frames and… read more here.
Keywords: sparse semantic; semantic map; map building; map ... See more keywords
Sparse Semantic Map-Based Monocular Localization in Traffic Scenes Using Learned 2D-3D Point-Line Correspondences
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3207800
Abstract: Vision-based localization in a prior map is of crucial importance for autonomous vehicles. Given a query image, the goal is to estimate the camera pose corresponding to the prior map, and the key is the… read more here.
Keywords: localization; based monocular; sparse semantic; map based ... See more keywords
COSEM: Collaborative Semantic Map Matching Framework for Autonomous Robots
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Industrial Electronics"
DOI: 10.1109/tie.2021.3070497
Abstract: Relative localization is a fundamental requirement for the coordination of multiple robots. To date, existing research in relative localization mainly depends on the extraction of low-level geometry features such as planes, lines, and points, which… read more here.
Keywords: map matching; semantic map; geometry; association ... See more keywords
NGLSFusion: Non-Use GPU Lightweight Indoor Semantic SLAM
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DOI: 10.3390/app13095285
Abstract: Perception of the indoor environment is the basis of mobile robot localization, navigation, and path planning, and it is particularly important to construct semantic maps in real time using minimal resources. The existing methods are… read more here.
Keywords: gpu lightweight; indoor; gpu; use gpu ... See more keywords