SymboSLAM: Semantic Map Generation in a Multi-Agent System

• URL: http://arxiv.org/abs/2403.15504v1
• Date: Fri, 22 Mar 2024 00:48:52 GMT
• Title: SymboSLAM: Semantic Map Generation in a Multi-Agent System
• Authors: Brandon Curtis Colelough,
• Abstract summary: Sub-symbolic artificial intelligence methods dominate the fields of environment-type classification and Simultaneous Localisation and Mapping. This paper proposes a novel approach to environment-type classification through Symbolic Simultaneous Localisation and Mapping, SymboSLAM, to bridge the explainability gap.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Sub-symbolic artificial intelligence methods dominate the fields of environment-type classification and Simultaneous Localisation and Mapping. However, a significant area overlooked within these fields is solution transparency for the human-machine interaction space, as the sub-symbolic methods employed for map generation do not account for the explainability of the solutions generated. This paper proposes a novel approach to environment-type classification through Symbolic Simultaneous Localisation and Mapping, SymboSLAM, to bridge the explainability gap. Our method for environment-type classification observes ontological reasoning used to synthesise the context of an environment through the features found within. We achieve explainability within the model by presenting operators with environment-type classifications overlayed by a semantically labelled occupancy map of landmarks and features. We evaluate SymboSLAM with ground-truth maps of the Canberra region, demonstrating method effectiveness. We assessed the system through both simulations and real-world trials.

Related papers

• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• 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)
• Distributed Bayesian Learning of Dynamic States [65.7870637855531]
  The proposed algorithm is a distributed Bayesian filtering task for finite-state hidden Markov models. It can be used for sequential state estimation, as well as for modeling opinion formation over social networks under dynamic environments.
  arXiv  Detail & Related papers  (2022-12-05T19:40:17Z)
• Image Synthesis via Semantic Composition [74.68191130898805]
  We present a novel approach to synthesize realistic images based on their semantic layouts. It hypothesizes that for objects with similar appearance, they share similar representation. Our method establishes dependencies between regions according to their appearance correlation, yielding both spatially variant and associated representations.
  arXiv  Detail & Related papers  (2021-09-15T02:26:07Z)
• Map completion from partial observation using the global structure of multiple environmental maps [4.627706451989238]
  This paper proposes a novel SLAM method, map completion network-based SLAM (MCN-SLAM), based on a probabilistic generative model incorporating deep neural networks for map completion. We show in experiments that the proposed method can estimate the environment map 1.3 times better than the previous SLAM methods in the situation of partial observation.
  arXiv  Detail & Related papers  (2021-03-16T13:48:37Z)
• Localising In Complex Scenes Using Balanced Adversarial Adaptation [19.160686658569507]
  Domain adaptation and generative modelling have collectively mitigated the expensive nature of data collection and labelling. We study the performance gap that exists between representations optimised for localisation on simulation environments and the application of such representations in a real-world setting.
  arXiv  Detail & Related papers  (2020-11-09T00:40:50Z)
• Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments [17.276441789710574]
  The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. This paper presents a method to solve the loop closure problem using only spatial information.
  arXiv  Detail & Related papers  (2020-09-01T04:41:40Z)
• Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty Regularization [73.03956876752868]
  We propose a principled and end-to-end train-able framework to allow the network to pay attention to other parts of the object. Specifically, we introduce the mixup data augmentation scheme into the classification network and design two uncertainty regularization terms to better interact with the mixup strategy.
  arXiv  Detail & Related papers  (2020-08-03T21:19:08Z)
• Neural Topological SLAM for Visual Navigation [112.73876869904]
  We design topological representations for space that leverage semantics and afford approximate geometric reasoning. We describe supervised learning-based algorithms that can build, maintain and use such representations under noisy actuation.
  arXiv  Detail & Related papers  (2020-05-25T17:56:29Z)
• Applying Rule-Based Context Knowledge to Build Abstract Semantic Maps of Indoor Environments [2.4214518935746185]
  We propose a method that combines data driven MCMC samplingand inference using rule-based context knowledge for data abstraction. The product of our system is a parametric abstract model of the perceived environment. Experiments on real world data show promising results and thus confirm the usefulness of our system.
  arXiv  Detail & Related papers  (2020-02-21T20:56:02Z)

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.