Proceedings ArticleDOI
Hierarchies of octrees for efficient 3D mapping
Kai M. Wurm,Daniel Hennes,Dirk Holz,Radu Bogdan Rusu,Cyrill Stachniss,Kurt Konolige,Wolfram Burgard +6 more
- pp 4249-4255
TLDR
A novel multi-resolution approach to efficiently mapping 3D environments that models the environment as a hierarchy of probabilistic 3D maps, in which each submap is updated and transformed individually.Abstract:
In this paper, we present a novel multi-resolution approach to efficiently mapping 3D environments. Our representation models the environment as a hierarchy of probabilistic 3D maps, in which each submap is updated and transformed individually. In addition to the formal description of the approach, we present an implementation for tabletop manipulation tasks and an information-driven exploration algorithm for autonomously building a hierarchical map from sensor data. We evaluate our approach using real-world as well as simulated data. The results demonstrate that our method is able to efficiently represent 3D environments at high levels of detail. Compared to a monolithic approach, our maps can be generated significantly faster while requiring significantly less memory.read more
Citations
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Journal ArticleDOI
OctoMap: an efficient probabilistic 3D mapping framework based on octrees
TL;DR: An open-source framework to generate volumetric 3D environment models based on octrees and uses probabilistic occupancy estimation that represents not only occupied space, but also free and unknown areas and an octree map compression method that keeps the 3D models compact.
An Efficient Probabilistic 3D Mapping Framework Based on Octrees
TL;DR: In this paper, an open-source framework is presented to generate volumetric 3D environ- ment models based on octrees and uses probabilistic occupancy estimation, which explicitly repre- sents not only occupied space, but also free and unknown areas.
Journal ArticleDOI
Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery
Margarita Grinvald,Fadri Furrer,Tonci Novkovic,Jen Jen Chung,Cesar Cadena,Roland Siegwart,Juan Nieto +6 more
TL;DR: This work presents an approach to incrementally build volumetric object-centric maps during online scanning with a localized RGB-D camera and demonstrates that the proposed approach for building instance-level semantic maps is competitive with state-of-the-art methods, while additionally able to discover objects of unseen categories.
Proceedings ArticleDOI
Information-theoretic exploration with Bayesian optimization
TL;DR: This work proposes a novel approach to predict mutual information (MI) using Bayesian optimization, and demonstrates that the proposed method provides not only computational efficiency and rapid map entropy reduction, but also robustness in comparison with competing approaches.
Proceedings ArticleDOI
Toward autonomous mapping and exploration for mobile robots through deep supervised learning
TL;DR: This work proposes a novel and time-efficient approach to predict the most informative sensing action using a deep neural network, and evaluates the performance of deep neural networks on the autonomous exploration of two-dimensional workspaces.
References
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Book
Information Theory, Inference and Learning Algorithms
TL;DR: A fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
Book
Information theory, inference, and learning algorithms
TL;DR: In this paper, the mathematics underpinning the most dynamic areas of modern science and engineering are discussed and discussed in a fun and exciting textbook on the mathematics underlying the most important areas of science and technology.
Proceedings ArticleDOI
High resolution maps from wide angle sonar
Hans P. Moravec,Alberto Elfes +1 more
TL;DR: The use of multiple wide-angle sonar range measurements to map the surroundings of an autonomous mobile robot deals effectively with clutter, and can be used for motion planning and for extended landmark recognition.
Journal ArticleDOI
Geometric modeling using octree encoding
TL;DR: Efficient (linear time) algorithms have been developed for the Boolean operations, geometric operations,translation, scaling and rotation, N-dimensional interference detection, and display from any point in space with hidden surfaces removed.
Journal ArticleDOI
Towards 3D Point cloud based object maps for household environments
TL;DR: The novel techniques include statistical analysis, persistent histogram features estimation that allows for a consistent registration, resampling with additional robust fitting techniques, and segmentation of the environment into meaningful regions.