ALIW-farm

This dataset comprises multi-sensor data collected from a custom unmanned ground vehicle (UGV) platform navigating through diverse unstructured agricultural environments, including open farmlands, papaya groves, and GPS-denied betel nut forests.
Sensor Configuration:

LiDAR: Livox Mid-360 with integrated IMU (Inertial Measurement Unit)
Wheel Odometry: High-precision wheel encoders for linear and angular velocity measurements
Extrinsic Calibration: Pre-calibrated spatial relationships between LiDAR-IMU and wheel odometry frames

Environmental Coverage:
The dataset captures challenging scenarios typical of agricultural robotics applications:

Open farmland: Feature-sparse arable land with sparse geometric constraints
Orchard environments: Papaya groves with dense canopy structures
GPS-denied areas: Betel nut forests with severe signal occlusion

Data Characteristics:

High-frequency IMU measurements for state propagation
Wheel odometry data providing velocity and scale constraints
3D LiDAR point clouds exhibiting non-uniform spatial distribution
Severe motion disturbances and terrain vibrations representative of real-world farm operations

Intended Use:
This dataset is specifically designed for evaluating tightly-coupled multi-sensor SLAM algorithms (LiDAR-IMU-Wheel fusion) in geometrically degraded agricultural environments.It serves as a complementary benchmark to existing public datasets (e.g., CitrusFarm) by providing:

Diverse environmental diversity across different crop types
Explicit LiDAR-IMU extrinsic parameters
Real-world validation scenarios for robustness analysis

File Format:
ROS bag format with synchronized sensor topics including IMU raw data, wheel encoder readings, and LiDAR point cloud streams.