3 accepted papers
Autoregressive generative models for irregularly sampled clinical time-series data are increasingly used for zero-shot risk forecasting. Prior work typically adopts a single fine-grained discretization of time, where tokens are generated at one fixed, pre-determined, temporal resolution. We demonstr…
Because scanning-LIDAR sensors require finite time to create a point cloud, sensor motion during a scan warps the resulting image, a phenomenon known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">motion distortion</i> or <italic xmlns:mml="http:
In this letter we propose an approach to mitigate shadowing errors in LIDAR scan matching, by introducing a preprocessing step based on spherical gridding. Because the grid aligns with the LIDAR beam, it is relatively easy to eliminate shadow edges which cause systematic errors in LIDAR scan matchin