I-LOAM: Intensity Enhanced LiDAR Odometry and Mapping
Yeong Sang Park, Hyesu Jang, Ayoung Kim
Abstract
In this paper, we introduce an extension to the existing LiDAR Odometry and Mapping (LOAM) [1] by additionally considering LiDAR intensity. In an urban environment, planar structures from buildings and roads often introduce ambiguity in a certain direction. Incorporation of the intensity value to the cost function prevents divergence occurence from this structural ambiguity, thereby yielding better odometry and mapping in terms of accuracy. Specifically, we have updated the edge and plane point correspondence search to include intensity. This simple but effective strategy shows meaningful improvement over the existing LOAM. The proposed method is validated using the KITTI dataset.
Topics & Concepts
OdometryLidarComputer scienceArtificial intelligenceAmbiguityRemote sensingIntensity (physics)Computer visionSegmentationLoamGeographyGeologyRobotOpticsMobile robotPhysicsSoil waterSoil scienceProgramming languageRobotics and Sensor-Based LocalizationRemote Sensing and LiDAR Applications3D Surveying and Cultural Heritage