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Found 9 papers, 1 papers with code
Learning High-level Semantic-Relational Concepts for SLAM
Concretely, our previous work, Situational Graphs (S-Graphs+), a pioneer in jointly leveraging semantic relationships in the factor optimization process, relies on semantic entities such as Planes and Rooms, whose relationship is mathematically defined.
Faster Optimization in S-Graphs Exploiting Hierarchy
A global optimization of the compressed graph is performed every time a loop closure is detected.
Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs
Firstly, we develop a method for converting the plan of a building into what we denote as an architectural graph (A-Graph).
S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations
In this paper, we present an evolved version of Situational Graphs, which jointly models in a single optimizable factor graph (1) a pose graph, as a set of robot keyframes comprising associated measurements and robot poses, and (2) a 3D scene graph, as a high-level representation of the environment that encodes its different geometric elements with semantic attributes and the relational information between them.
Advanced Situational Graphs for Robot Navigation in Structured Indoor Environments
Mobile robots extract information from its environment to understand their current situation to enable intelligent decision making and autonomous task execution.
Visual SLAM: What are the Current Trends and What to Expect?
In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map generation.
RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry
To this end, we match 2D keypoints between consecutive frames using pre-trained deep networks, Superpoint and Superglue, while training a network for depth and pose estimation using an unsupervised training protocol.
Situational Graphs for Robot Navigation in Structured Indoor Environments
Mobile robots should be aware of their situation, comprising the deep understanding of their surrounding environment along with the estimation of its own state, to successfully make intelligent decisions and execute tasks autonomously in real environments.
From SLAM to Situational Awareness: Challenges and Survey
The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation.
