Tools with HeLiPR Dataset

File Player

• The File Player is an application designed to convert bin, csv, or txt files into ROS messages.

• Features of the File Player for HeLiPR:

  • Display of Play Time & End Time: This allows users to anticipate the time left for the completion of the playback.

  • Rosbag File Saving: The application offers the convenience of simultaneously saving the played content as a rosbag file.

  • Loop Functionality: For users who wish to review the content multiple times, there's an option for repeated playback.

  • Playback Speed Control: Users can control the speed of the playback, allowing for quicker reviews of the content.

  • Navigation Controls: Through the use of a slide bar and play time indicators, users can effortlessly jump to any desired point in the playback.

• With these features, the File Player enhances the user experience and provides flexibility for those working with the HeLiPR dataset.

HeLiPR-Pointcloud-Toolbox (w/ frame transformer from INS to LiDAR)

• The HeLiPR Pointcloud Toolbox is a sophisticated software suite tailored for processing and analyzing the HeLiPR (Heterogeneous LiDAR Place Recognition) dataset. This toolbox offers robust functionalities for manipulating point cloud data from various LiDAR sensors, making it an essential tool for researchers in place recognition, SLAM (Simultaneous Localization and Mapping), and other related fields.

  • Versatile LiDAR Data Processing: Supports Ouster, Velodyne, Livox, and Aeva LiDAR types in the HeLiPR dataset.

  • Efficient File Handling: Reads .bin files from different LiDAR types and outputs processed data in .pcd format.

  • Point Cloud Undistortion: Implements BsplineSE3 for trajectory interpolation, undistorts point clouds accordingly.

  • Point Cloud Accumulation: Accumulates multiple point cloud scans with user-defined thresholds, aiding in place recognition tasks.

  • Point Cloud Saver: Save pointcloud with accumulation or undistortion with user-defined distances, aiding in place recognition tasks.

  • Interactive User Experience: Prompts for input paths and processing parameters for a customized workflow with visual progress tracking such as progress bar and visualizer.

  • Trajectory Interpolation: Accurately interpolates trajectory data for precise point cloud mapping.

Usage of HeLiPR-Pointcloud-Toolbox

• python/transformINStoLiDAR.py: Transforms INS data to the LiDAR frame, aiding in trajectory creation and undistortion.

  • Input: CSV file with latitude, longitude, altitude data (Inertial_data.csv from HeLiPR dataset).

  • Output: Text file with trajectory data in LiDAR frame.

• python/binVisualizer.py: Visualize the bin file using open3d.

  • Input: bin file from HeLiPR dataset.

  • Output: visualize the 3D pointcloud via open3d.

• src/PointCloudProcessor.cpp: Processes point clouds from LiDAR data.

  • Function: Converts bin data to undistorted point clouds, accumulates them based on user-defined thresholds.

  • Input: Bin files from specific LiDAR and trajectory file from python/transformINStoLiDAR.py.

  • Output: Undistorted and accumulated point clouds.

MA-LIO

• MA-LIO is designed to serve as a Multiple LiDAR odometry package. While its foundational design does not directly target place recognition, the capabilities of the package indicate that our dataset can be effectively employed in LiDAR SLAM (Simultaneous Localization and Mapping) field.

Key Features and Benefits of MA-LIO for HeLiPR Dataset:

• Support for Asynchronous Multiple LiDAR: MA-LIO is tailored to support asynchronous data from multiple LiDARs, making it a perfect fit for the HeLiPR dataset.

• Precision in Mapping: Despite the asynchronicity of the data, MA-LIO showcases its capability in ensuring accurate alignment and mapping. As evidenced in the detailed map on the right, it effectively aligns streetlights, bus stops, and trees, underscoring its precision.

• Large-Scale SLAM Researches: The partial map provided further indicates that the HeLiPR dataset, when used with MA-LIO, can be a valuable resource for SLAM research on a large scale.

• In essence, MA-LIO, with its capability to handle multiple asynchronous LiDAR data and its precision in mapping, offers researchers and practitioners a robust tool for extensive SLAM applications, especially when paired with the HeLiPR dataset.