Hybrid topological/metric approach to slam

Abstract — We present a new topological/metric approach to solving the Simultaneous Localisation and Mapping problem. The map is represented as a graph- nodes are local map frames, and edges are transformations between adjacent map frames. The underlying local mapping algorithm is FastSLAM. The local maps and transformations are modelled by sets of particles. There is no global map frame, each map’s uncertainties are restricted to its own map frame. The loop closing is achieved via efficient map matching. We demonstrate our algorithm running in real-time in an indoor environment using a laser range sensor. I

Hybrid Topological/Metric Approach to SLAM

We present a new topological/metric approach to solving the Simultaneous Localisation and Mapping problem. The map is represented as a graph - nodes are local map frames, and edges are transformations between adjacent map frames. The underlying local mapping algorithm is FastSLAM. The local maps and transformations are modelled by sets of particles. There is no global map frame, each map's uncertainties are restricted to its own map frame. The loop closing is achieved via efficient map matching. We demonstrate our algorithm running in real-time in an indoor environment using a laser range sensor

Digital Earth Australia notebooks and tools repository

The Digital Earth Australia notebooks and tools repository ('DEA notebooks') hosts Jupyter Notebooks, Python scripts and workflows for analysing Digital Earth Australia (DEA) satellite data and derived products. The repository is intended to provide a guide to getting started with DEA, and to showcase the wide range of geospatial analyses that can be achieved using DEA data and open-source software including Open Data Cube and xarray.If you use any of the notebooks, code or tools in this repository in your work, please reference them using the following citation

corteva/rioxarray: 0.15.1 Release

<h2>What's Changed</h2> <ul> <li>More robust handling of GCPs geojson #731 by @Kirill888 in https://github.com/corteva/rioxarray/pull/735</li> <li>DEP: Support Python 3.10-3.12 by @snowman2 in https://github.com/corteva/rioxarray/pull/723</li> <li>DEP: rasterio 1.3+ by @snowman2 in https://github.com/corteva/rioxarray/pull/725</li> <li>DEP: Update to pyproj 3.3+ by @snowman2 in https://github.com/corteva/rioxarray/pull/727</li> <li>DEP: xarray 2022.3.0+ & numpy 1.23+ by @snowman2 in https://github.com/corteva/rioxarray/pull/728</li> </ul> <h2>New Contributors</h2> <ul> <li>@dependabot made their first contribution in https://github.com/corteva/rioxarray/pull/696</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/corteva/rioxarray/compare/0.15.0...0.15.1</p&gt