LOGIC: LiDAR-Only Geometric-Intensity Channel-based Drivable Area Estimation in Urban Environments

Autonomous vehicles today are highly dependent on high-definition maps of the area they navigate in. This creates economic barriers in the way of the massive deployment of this technology, which can be overcome by estimating the drivable area onboard the autonomous vehicle. This task has been exhaustively explored using RGB cameras while LiDAR-only approaches are less common. In this paper, we propose LOGIC: a LiDAR-Only Geometric-Intensity Channel-based method for drivable area estimation. Our approach first obtains proposals of the drivable area by leveraging different pointcloud analysis procedures, including geometrical features, intensity evaluation and relations between neighbouring points. Then, these proposals are modeled as probabilistic drivability estimations and fused over time on a grid. This way of proceeding allows for a comprehensive analysis of the LiDAR data while also producing robust estimations. In addition, grid-level fusion enables the potential accommodation of additional navigable area detection methods or sensor inputs. Our method is able to match the performance of state-of-the-art methods without training or case-by-case parameter tuning while being tested on over 37000 LiDAR frames in the Waymo Open Dataset [1].