SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory
نویسندگان
چکیده
منابع مشابه
Vision-Enhanced Lidar Odometry and Mapping
Vision-Enhanced Lidar Odometry and Mapping (VELO) is a new algorithm for simultaneous localization and mapping using a set of cameras and a lidar. By tightly coupling sparse visual odometry and lidar scanmatching, VELO is able to achieve reduced dri error compared to using either one or the other method. Moreover, the algorithm is capable of functioningwhen either the lidar or the camera is bli...
متن کاملIntegration of Lidar, Landsat Etm+ and Forest Inventory Data for Regional Forest Mapping
Recent work has established the utility of waveform sampling lidar for predicting forest structural attributes. Nevertheless, serious obstacles to its wide-spread use still exist. They include the lack of waveform sampling lidar sensors capable of measuring forest canopy structure over large extents, and the practical difficulty of developing widely applicable relationships to predict forest st...
متن کاملMapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data
As a large carbon pool, global forest ecosystems are a critical component of the global carbon cycle. Accurate estimations of global forest aboveground biomass (AGB) can improve the understanding of global carbon dynamics and help to quantify anthropogenic carbon emissions. Light detection and ranging (LiDAR) techniques have been proven that can accurately capture both horizontal and vertical f...
متن کاملLow-drift and real-time lidar odometry and mapping
Here we propose a real-time method for low-drift odometry andmapping using rangemeasurements from a 3D laser scanner moving in 6-DOF. The problem is hard because the range measurements are received at different times, and errors in motion estimation (especially without an external reference such asGPS) causemis-registration of the resulting point cloud. To date, coherent 3D maps have been built...
متن کاملLOAM: Lidar Odometry and Mapping in Real-time
We propose a real-time method for odometry and mapping using range measurements from a 2-axis lidar moving in 6-DOF. The problem is hard because the range measurements are received at different times, and errors in motion estimation can cause mis-registration of the resulting point cloud. To date, coherent 3D maps can be built by off-line batch methods, often using loop closure to correct for d...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Robotics and Automation Letters
سال: 2020
ISSN: 2377-3766,2377-3774
DOI: 10.1109/lra.2019.2963823