多传感器数据融合数据集

采用拓展卡尔曼滤波传感器融合算法，基于可见光相机、深度相机、激光雷达等异构传感器进行里程计数据融合，修正里程计数据进行SLAM环境地图构建。使用rosbag录制试验过程中的场景回放数据，构建多传感器数据融合数据集。包括机器人GPS数据、位姿数据、相机数据、激光雷达数据、融合后的位姿数据等信息，数据量7.2GB。

This multi-sensor data fusion dataset is constructed using the Extended Kalman Filter (EKF) based sensor fusion algorithm, which fuses odometry data via heterogeneous sensors including visible-light cameras, depth cameras and LiDAR, and corrects odometry data to build SLAM environment maps. Scene replay data collected during experiments was recorded using rosbag. The dataset encompasses robot GPS data, pose data, camera data, LiDAR data, fused pose data and other related information, with a total size of 7.2 GB.

搜集汇总

背景与挑战

背景概述

该数据集是一个多传感器数据融合数据集，采用可见光相机、深度相机、激光雷达等异构传感器进行里程计数据融合，用于SLAM环境地图构建。数据集包含机器人GPS数据、位姿数据、相机数据、激光雷达数据等，数据量为7.12GB，格式为bag和docx。