N2 - Existing RTLS solutions are mostly based on either wireless technologies, fiducial markers, or Lidar-based simultaneous localization and mapping (SLAM), and inevitably suffer from some drawbacks such as low accuracy, reliance on existing facilities, labor-intensive environment instrumentation, or high economic cost. © 2019 American Society of Civil Engineers.Ĭopyright 2020 Elsevier B.V., All rights reserved. T1 - Enhancing Visual SLAM with Occupancy Grid Mapping for Real-Time Locating Applications in Indoor GPS-Denied Environments The localization accuracy of the system is evaluated with a marker-based method, which proves its feasibility and applicability in indoor building and construction applications.", These designs not only allow users to interact with the system but also open up the possibilities of path planning and continuous navigation based on feature-based vSLAM. Besides the original sparse feature map built by the visual SLAM (vSLAM), the proposed system builds and maintains an additional 2D occupancy grid map (OGM) and overlays it with real-time 2D camera pose and virtual laser scan for 2D localization. This paper introduces an ORB2 RGB-D SLAM based indoor RTLS that can be readily adapted and applied to building or civil infrastructure applications while addressing the above limitations. The localization accuracy of the system is evaluated with a marker-based method, which proves its feasibility and applicability in indoor building and construction applications.Ībstract = "Existing RTLS solutions are mostly based on either wireless technologies, fiducial markers, or Lidar-based simultaneous localization and mapping (SLAM), and inevitably suffer from some drawbacks such as low accuracy, reliance on existing facilities, labor-intensive environment instrumentation, or high economic cost. The experimental results show 6.6% improvement in the global grid map and it is also found that the proposed approach is consuming nearly 16.5% less computation time as compared to the conventional approach of occupancy grid mapping for the indoor environments.Existing RTLS solutions are mostly based on either wireless technologies, fiducial markers, or Lidar-based simultaneous localization and mapping (SLAM), and inevitably suffer from some drawbacks such as low accuracy, reliance on existing facilities, labor-intensive environment instrumentation, or high economic cost. In this paper the approach is experimented for the office environment and the model is used for grid mapping. To achieve the grid map with improved accuracy, the sonar information is further updated by using a Bayesian approach. Further, the inconsistency estimation in sonar measurement has been evaluated and eliminated by fuzzy rules based model. Here, a novel algorithm is proposed which is capable of discarding the unreliable sonar sensor information generated due to specular reflection. Such uncertainty reduction is often required in the occupancy grid mapping where the false sensory information can lead to poor performance. This paper addresses the improved method for sonar sensor modeling which reduces the specular reflection uncertainty in the occupancy grid.
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