Extracting Semantic Indoor Maps from Occupancy Grids

• URL: http://arxiv.org/abs/2002.08348v1
• Date: Wed, 19 Feb 2020 18:52:27 GMT
• Title: Extracting Semantic Indoor Maps from Occupancy Grids
• Authors: Ziyuan Liu, Georg von Wichert
• Abstract summary: We focus on the semantic mapping of indoor environments. We propose a method to extract an abstracted floor plan from typical grid maps using Bayesian reasoning. We demonstrate the effectiveness of the approach using real-world data.
• Score: 2.4214518935746185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The primary challenge for any autonomous system operating in realistic, rather unconstrained scenarios is to manage the complexity and uncertainty of the real world. While it is unclear how exactly humans and other higher animals master these problems, it seems evident, that abstraction plays an important role. The use of abstract concepts allows to define the system behavior on higher levels. In this paper we focus on the semantic mapping of indoor environments. We propose a method to extract an abstracted floor plan from typical grid maps using Bayesian reasoning. The result of this procedure is a probabilistic generative model of the environment defined over abstract concepts. It is well suited for higher-level reasoning and communication purposes. We demonstrate the effectiveness of the approach using real-world data.

Related papers

• Mapping High-level Semantic Regions in Indoor Environments without Object Recognition [50.624970503498226]
  The present work proposes a method for semantic region mapping via embodied navigation in indoor environments. To enable region identification, the method uses a vision-to-language model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location.
  arXiv  Detail & Related papers  (2024-03-11T18:09:50Z)
• Neural Causal Abstractions [63.21695740637627]
  We develop a new family of causal abstractions by clustering variables and their domains. We show that such abstractions are learnable in practical settings through Neural Causal Models. Our experiments support the theory and illustrate how to scale causal inferences to high-dimensional settings involving image data.
  arXiv  Detail & Related papers  (2024-01-05T02:00:27Z)
• Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning [83.41487567765871]
  Skipper is a model-based reinforcement learning framework. It automatically generalizes the task given into smaller, more manageable subtasks. It enables sparse decision-making and focused abstractions on the relevant parts of the environment.
  arXiv  Detail & Related papers  (2023-09-30T02:25:18Z)
• AI planning in the imagination: High-level planning on learned abstract search spaces [68.75684174531962]
  We propose a new method, called PiZero, that gives an agent the ability to plan in an abstract search space that the agent learns during training. We evaluate our method on multiple domains, including the traveling salesman problem, Sokoban, 2048, the facility location problem, and Pacman.
  arXiv  Detail & Related papers  (2023-08-16T22:47:16Z)
• ALSO: Automotive Lidar Self-supervision by Occupancy estimation [70.70557577874155]
  We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds. The core idea is to train the model on a pretext task which is the reconstruction of the surface on which the 3D points are sampled. The intuition is that if the network is able to reconstruct the scene surface, given only sparse input points, then it probably also captures some fragments of semantic information.
  arXiv  Detail & Related papers  (2022-12-12T13:10:19Z)
• Abstracting Noisy Robot Programs [17.04153879817609]
  We describe an approach to abstraction of probabilistic and dynamic systems. Based on a variant of the situation calculus with probabilistic belief, we define a notion of bisimulation. We obtain abstract Golog programs that omit unnecessary details and which can be translated back to a detailed program for actual execution.
  arXiv  Detail & Related papers  (2022-04-07T16:04:19Z)
• CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning [68.74447489372037]
  We present a high-fidelity simulation environment that is designed for developing algorithms for causal discovery and counterfactual reasoning. A core component of our work is to introduce textitagency, such that it is simple to define and create complex scenarios. We perform experiments with three state-of-the-art methods to create baselines and highlight the affordances of this environment.
  arXiv  Detail & Related papers  (2021-06-25T00:21:41Z)
• Towards a Mathematical Theory of Abstraction [0.0]
  We provide a precise characterisation of what an abstraction is and, perhaps more importantly, suggest how abstractions can be learnt directly from data. Our results have deep implications for statistical inference and machine learning and could be used to develop explicit methods for learning precise kinds of abstractions directly from data.
  arXiv  Detail & Related papers  (2021-06-03T13:23:49Z)
• Learning Discrete State Abstractions With Deep Variational Inference [7.273663549650618]
  We propose a method for learning approximate bisimulations, a type of state abstraction. We use a deep neural encoder to map states onto continuous embeddings. We map these embeddings onto a discrete representation using an action-conditioned hidden Markov model.
  arXiv  Detail & Related papers  (2020-03-09T17:58:27Z)
• Online Semantic Exploration of Indoor Maps [8.388111003458187]
  We propose a method to extract an abstracted floor plan from typical grid maps using Bayesian reasoning. The result is a probabilistic generative model of the environment defined over abstract concepts.
  arXiv  Detail & Related papers  (2020-02-21T21:07:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.