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 us 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.

pdf/extracting-semantic-indoor-maps-from-occupancy-grids-3cxqm1jrp0.pdf

Extracting Semantic Indoor Maps from Occupancy Grids

Smart cities use the Internet of Things (IoT) devices such as connected sensors, lights, and meters to collect and analyze data to improve infrastructure, public utilities, and services. However, the true potential of smart cities cannot be leveraged without addressing many security concerns. In particular, there is a significant challenge for provisioning a reliable access control solution to share IoT data among various users across organizations. We present a novel entitlement-based blockchain-enabled access control architecture that can be used for smart cities (and for any ap-plication domains that require large-scale IoT deployments). Our proposed entitlement-based access control model is flexible as it facilitates a resource owner to safely delegate access rights to any entities beyond the trust boundary of an organization. The detailed design and implementation on Ethereum blockchain along with a qualitative evaluation of the security and access control aspects of the proposed scheme are presented in the paper. The experimental results from private Ethereum test networks demonstrate that our proposal can be easily implemented with low latency. This validates that our proposal is applicable to use in the real world IoT environments.

Entitlement-Based Access Control for Smart Cities Using Blockchain.

This paper presents a feature-based map merging approach through detecting, describing, and matching geometric features between the maps. The key contribution of this work is to identify the effective method for merging maps that are developed by varying the grid resolution and scan rate parameters. The map data sets are created by Lidar mounted on the QCar mobile robot platform. The comparison of feature detection methods to register map images at different scale is presented. Finally, the effectiveness of the proposed approach is validated based on the map fusion assumptions using real-world data. Also, SLAM (robot motion) is carried out on the merged global map (developed by proposed map fusion method).

Lidar-Based Cooperative SLAM with Different Parameters

This paper presents a feature-based map merging approach through detecting, describing, and matching geometric features between the maps. The key contribution of this work is to identify the effective method for merging maps that are developed by varying the grid resolution and scan rate parameters. The map data sets are created by Lidar mounted on the QCar mobile robot platform. The comparison of feature detection methods to register map images at different scale is presented. Finally, the effectiveness of the proposed approach is validated based on the map fusion assumptions using real-world data. Also, SLAM (robot motion) is carried out on the merged global map (developed by proposed map fusion method). 

Map fusion is a key challenge in multi-robot SLAM systems, with feature-based methods often leading to mismatches when handling maps of different resolutions. This paper introduces a density clustering-based map fusion method that reduces mismatches by clustering features before matching them. Experiments on public datasets demonstrate that the proposed method effectively merges grid maps of varying resolutions, offering improved efficiency and accuracy compared to traditional feature-based methods.

Grid Map Fusion of Different Resolutions Based on Density Clustering

Building an accurate map is essential for autonomous robot navigation in the environment without GPS. Compared with single-robot, the multiple-robot system has much better performance in terms of accuracy, efficiency and robustness for the simultaneous localization and mapping (SLAM). As a critical component of multiple-robot SLAM, the problem of map merging still remains a challenge. To this end, this article casts it into point set registration problem and proposes an effective map merging method based on the context-based descriptors and correspondence expansion. It first extracts interest points from grid maps by the Harris corner detector. By exploiting neighborhood information of interest points, it automatically calculates the maximum response radius as scale information to compute the context-based descriptor, which includes eigenvalues and normals computed from local structures of each interest point. Then, it effectively establishes origin matches with low precision by applying the nearest neighbor search on the context-based descriptor. Further, it designs a scale-based corresponding expansion strategy to expand each origin match into a set of feature matches, where one similarity transformation between two grid maps can be estimated by the Random Sample Consensus algorithm. Subsequently, a measure function formulated from the trimmed mean square error is utilized to confirm the best similarity transformation and accomplish the coarse map merging. Finally, it utilizes the scaling trimmed iterative closest point algorithm to refine initial similarity transformation so as to achieve accurate merging. As the proposed method considers scale information in the context-based descriptor, it is able to merge grid maps in diverse resolutions. Experimental results on real robot datasets demonstrate its superior performance over other related methods on accuracy and robustness.

Merging Grid Maps in Diverse Resolutions by the Context-based Descriptor

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Merging Grid Maps in Diverse Resolutions by the Context-based Descriptor