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Moving to Signal is hard

Efficient Manipulation-Enhanced Semantic Mapping With Uncertainty-Informed Action Selection
Nils Dengler, Jesper M\"ucke, Rohit Menon, Maren Bennewitz
https://arxiv.org/abs/2506.02286

Efficient Manipulation-Enhanced Semantic Mapping With Uncertainty-Informed Action Selection
Service robots operating in cluttered human environments such as homes, offices, and schools cannot rely on predefined object arrangements and must continuously update their semantic and spatial estimates while dealing with possible frequent rearrangements. Efficient and accurate mapping under such conditions demands selecting informative viewpoints and targeted manipulations to reduce occlusions and uncertainty. In this work, we present a manipulation-enhanced semantic mapping framework for oc…

SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
Prithvish V Nembhani, Oliver Rhodes, Guangzhi Tang, Alexandra F Dobrita, Yingfu Xu, Kanishkan Vadivel, Kevin Shidqi, Paul Detterer Mario Konijnenburg, Gert-Jan van Schaik, Manolis Sifalakis, Zaid Al-Ars, Amirreza Yousefzadeh
https://…

SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
This paper introduces SENMap, a mapping and synthesis tool for scalable, energy-efficient neuromorphic computing architecture frameworks. SENECA is a flexible architectural design optimized for executing edge AI SNN/ANN inference applications efficiently. To speed up the silicon tape-out and chip design for SENECA, an accurate emulator, SENSIM, was designed. While SENSIM supports direct mapping of SNNs on neuromorphic architectures, as the SNN and ANNs grow in size, achieving optimal mapping fo…

Dynamic mapping from static labels: remote sensing dynamic sample generation with temporal-spectral embedding
Shuai Yuan, Shuang Chen, Tianwu Lin, Jie Wang, Peng Gong
https://arxiv.org/abs/2506.02574

Dynamic mapping from static labels: remote sensing dynamic sample generation with temporal-spectral embedding
Accurate remote sensing geographic mapping depends heavily on representative and timely sample data. However, rapid changes in land surface dynamics necessitate frequent updates, quickly rendering previously collected samples obsolete and imposing significant labor demands for continuous manual updates. In this study, we aim to address this problem by dynamic sample generation using existing single-date static labeled samples. We introduce TasGen, a two-stage automated framework to automaticall…

On $\theta$-extension of continuous mapping
Andrew Ryabikov
https://arxiv.org/abs/2506.02228 https://arxiv.org/pdf/2506.02228

On $θ$-extension of continuous mapping
We consider the problem of constructing a weakly-continuous mapping extending continuous mapping defined on a dense set of a topological space to the entire space. Theorem on necessary and sufficient conditions for the existence of such an extension are proved. The axioms of separation and compactness of spaces are not assumed.

Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges
Xiao-Yang Liu Yanglet, Yupeng Cao, Li Deng
https://arxiv.org/abs/2506.01973

Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges
Financial Large Language Models (FinLLMs), such as open FinGPT and proprietary BloombergGPT, have demonstrated great potential in select areas of financial services. Beyond this earlier language-centric approach, Multimodal Financial Foundation Models (MFFMs) can digest interleaved multimodal financial data, including fundamental data, market data, data analytics, macroeconomic, and alternative data (e.g., natural language, audio, images, and video). In this position paper, presented at the MFF…

This https://arxiv.org/abs/2506.00541 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

3D Trajectory Reconstruction of Moving Points Based on Asynchronous Cameras
Photomechanics is a crucial branch of solid mechanics. The localization of point targets constitutes a fundamental problem in optical experimental mechanics, with extensive applications in various missions of UAVs. Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties. Reconstructing the trajectories of points from asynchronous cameras is a significant challenge. It encompasses two coupled sub-problems: trajectory reconstruction and camera synchr…

This https://arxiv.org/abs/2506.01950 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

DualMap: Online Open-Vocabulary Semantic Mapping for Natural Language Navigation in Dynamic Changing Scenes
We introduce DualMap, an online open-vocabulary mapping system that enables robots to understand and navigate dynamically changing environments through natural language queries. Designed for efficient semantic mapping and adaptability to changing environments, DualMap meets the essential requirements for real-world robot navigation applications. Our proposed hybrid segmentation frontend and object-level status check eliminate the costly 3D object merging required by prior methods, enabling effi…

This https://arxiv.org/abs/2505.10018 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

LEMON-Mapping: Loop-Enhanced Large-Scale Multi-Session Point Cloud Merging and Optimization for Globally Consistent Mapping
Multi-robot collaboration is becoming increasingly critical and presents significant challenges in modern robotics, especially for building a globally consistent, accurate map. Traditional multi-robot pose graph optimization (PGO) methods ensure basic global consistency but ignore the geometric structure of the map, and only use loop closures as constraints between pose nodes, leading to divergence and blurring in overlapping regions. To address this issue, we propose LEMON-Mapping, a loop-enha…

Automatic Operation of an Articulated Dump Truck: State Estimation by Combined QZSS CLAS and Moving-Base RTK Using Multiple GNSS Receivers
Taro Suzuki, Shotaro Kojima, Kazunori Ohno, Naoto Miyamoto, Takahiro Suzuki, Kimitaka Asano, Tomohiro Komatsu, Hiroto Kakizaki
https://arxiv.org/abs/2506.02877

Automatic Operation of an Articulated Dump Truck: State Estimation by Combined QZSS CLAS and Moving-Base RTK Using Multiple GNSS Receivers
Labor shortage due to the declining birth rate has become a serious problem in the construction industry, and automation of construction work is attracting attention as a solution to this problem. This paper proposes a method to realize state estimation of dump truck position, orientation and articulation angle using multiple GNSS for automatic operation of dump trucks. RTK-GNSS is commonly used for automation of construction equipment, but in mountainous areas, mobile networks often unstable, …