Performing closed-loop grasping at close proximity to an object requires a large field of view.
Intra-day economic dispatch of an integrated microgrid is a fundamental requirement to integrate distributed generators.
We present a heterogeneous localization framework for solving radar global localization and pose tracking on pre-built lidar maps.
In this paper, we propose a LiDAR-based place recognition method, named Differentiable Scan Context with Orientation (DiSCO), which simultaneously finds the scan at a similar place and estimates their relative orientation.
In this paper, we propose an end-to-end deep learning framework for Radar Localization on Lidar Map (RaLL) to bridge the gap, which not only achieves the robust radar localization but also exploits the mature lidar mapping technique, thus reducing the cost of radar mapping.
Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge.