Area MaPPing
Area mapping encompasses the creation of spatial representations of environments using various sensor data, aiming for accurate localization and robust mapping in diverse conditions. Current research emphasizes improving the accuracy and efficiency of mapping algorithms, particularly through the integration of deep learning for feature extraction and improved outlier rejection, and the use of multi-sensor fusion (e.g., LiDAR, cameras, UWB) to enhance robustness. These advancements are crucial for autonomous systems in various fields, including robotics, autonomous vehicles, and environmental monitoring, enabling more reliable navigation and scene understanding.
Papers
Hierarchical Pose Estimation and Mapping with Multi-Scale Neural Feature Fields
Evgenii Kruzhkov, Alena Savinykh, Sven Behnke
Air-Ground Collaborative Robots for Fire and Rescue Missions: Towards Mapping and Navigation Perspective
Ying Zhang, Haibao Yan, Danni Zhu, Jiankun Wang, Cui-Hua Zhang, Weili Ding, Xi Luo, Changchun Hua, Max Q.-H. Meng
Mapping The Layers of The Ocean Floor With a Convolutional Neural Network
Guilherme G. D. Fernandes, Vitor S. P. P. Oliveira, João P. I. Astolfo
Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions
August Leander Høeg, Sophia W. Bardenfleth, Hans Martin Kjer, Tim B. Dyrby, Vedrana Andersen Dahl, Anders Dahl
SF-Loc: A Visual Mapping and Geo-Localization System based on Sparse Visual Structure Frames
Yuxuan Zhou, Xingxing Li, Shengyu Li, Chunxi Xia, Xuanbin Wang, Shaoquan Feng
Misalignments in AI Perception: Quantitative Findings and Visual Mapping of How Experts and the Public Differ in Expectations and Risks, Benefits, and Value Judgments
Philipp Brauner, Felix Glawe, Gian Luca Liehner, Luisa Vervier, Martina Ziefle