English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Recent developments in LiDAR sensors make mobile mapping fast and cost effective. These sensors generate a large amount of data\udwhich in turn improves the coverage and details of the map. Due to the limited range of the sensor, one has to collect a series of scans\udto build the entire map of the environment. If we have good GNSS coverage, building a map is a well addressed problem. But in an\udindoor environment, we have limited GNSS reception and an inertial solution, if available, can quickly diverge. In such situations,\udsimultaneous localization and mapping (SLAM) is used to generate a navigation solution and map concurrently. SLAM using point\udclouds possesses a number of computational challenges even with modern hardware due to the shear amount of data. In this paper, we\udpropose two strategies for minimizing the cost of computation and storage when a 3D point cloud is used for navigation and real-time\udmap building. We have used the 3D point cloud generated by Leica Geosystems's Pegasus Backpack which is equipped with Velodyne\udVLP-16 LiDARs scanners. To improve the speed of the conventional iterative closest point (ICP) algorithm, we propose a point cloud\udsub-sampling strategy which does not throw away any key features and yet significantly reduces the number of points that needs to be\udprocessed and stored. In order to speed up the correspondence finding step, a dual kd-tree and circular buffer architecture is proposed.\udWe have shown that the proposed method can run in real time and has excellent navigation accuracy characteristics

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

A FAST AND FLEXIBLE METHOD FOR META-MAP BUILDING FOR ICP BASED SLAM