Staff

Abstract:
This paper describes a software toolbox for LiDAR (Light Detection and Ranging) and LiDAR-Inertial Odometry qualitative and quantitative evaluation. We provide software as https://github.com/MapsHD organization with all necessary information at https://github.com/MapsHD/HDMapping. Our software contributions are a) ground truth data processing tool, b) dockerized state-of-the-art LO and LIO algorithms, c) multi-session data registration to common coordinate system, d) Absolute Pose Error (APE) and Relative Pose Error (RPE) metrics, e) import/export tools for easier 3D data handling and visualizing, e.g., in Cloud Compare software. This software is compatible with ROS1 (Robot Operating System) and ROS2 data formats. We show an example benchmark of LeGO-LOAM, LIO-SAM, FAST-LIO, DLO, VoxelMap, Faster-LIO, KISS-ICP, CT-ICP, SLICT, DLIO, GLIM, iG-LIO, LIO-EKF, I2EKF-LO, GenZ-ICP, RESPLE, odometry_ros_wrapper, Point-LIO, and LOAM-Livox algorithms. For all experiments we provide movies. The contribution of the paper is software-oriented LO/LIO algorithm benchmark suite. The novelty lies in the integration of multiple benchmarking steps into a unified framework, thus overall effort needed for qualitative and quantitative evaluation is reduced.

Keywords:
LiDAR odometry, LiDAR-inertial odometry, Benchmarking

Abstract:
Precise forest inventory in difficult terrain remains challenging due to mobility constraints and canopy occlusion. This paper presents an affordable air-ground mobile mapping system combining shoulder-mounted double LiDAR with a lightweight FPV drone-based LiDAR. We developed an extrinsic calibration method for dual orthogonally-mounted sensors and implemented a comprehensive processing pipeline incorporating LiDAR odometry, pose graph SLAM, and multi-view Normal Distributions Transform. Field ex-periments demonstrate successful air-ground data fusion for tree stem detection and dendrometric parameter extraction. The system was validated in extreme environments including a cave survey, proving versatility in difficult terrain. All software components are released as open-source tools: https://github.com/MapsHD/HDMapping.

Keywords:
UAV LiDAR, Forest Inventory, Under-Canopy Mapping, SLAM, Open Source

Abstract:
This paper introduces a method for 360 camera with 3D LiDAR calibration and synchronization with example on Insta360 X4 and LiVOX MID 360. Both data streams (camera and LiDAR) are recorded separately to reach interoperability, robustness, full resolution, and maximal FPS (Frames Per Second). The novelty is based on LED circle strip illuminating timestamp, thus this information is recorded by camera. The timestamp is presented using gray code utilizing individually addressed LED strips. The data signal for LED is prepared by micro-controller. Microcontroller (ESP-8285) communicates using USB. We incorporated ResNet-18 based binary classifier to classify LEDs. Our method can efficiently assign timestamps with a resolution of 100 ms.
To fulfill the process of 3D point cloud colorization, we implemented camera to LiDAR calibration procedure requiring manually assigning 5 pairs of image to 3D point correspondences. Our method is independent from hardware, thus it can be used also for affordable one making it more accessible and easy to use.

Keywords:
mobile mapping, sensor synchronization, 360 camera, 3D lidar