Analysis of LiDAR Configurations on Off-road Semantic Segmentation Performance

TL;DR

This study examines how different LiDAR sensor configurations affect the accuracy of 3D semantic segmentation models in off-road environments, highlighting the importance of sensor choice and domain shifts.

Contribution

It provides a systematic analysis of LiDAR configuration impacts on segmentation performance, emphasizing the effects of sensor resolution and domain shifts in off-road scenarios.

Findings

Sensor and spatial domain shifts significantly affect model performance.

Models perform better when trained and tested on the same sensor type.

Higher-resolution sensors generally improve segmentation accuracy.

Abstract

This paper investigates the impact of LiDAR configuration shifts on the performance of 3D LiDAR point cloud semantic segmentation models, a topic not extensively studied before. We explore the effect of using different LiDAR channels when training and testing a 3D LiDAR point cloud semantic segmentation model, utilizing Cylinder3D for the experiments. A Cylinder3D model is trained and tested on simulated 3D LiDAR point cloud datasets created using the Mississippi State University Autonomous Vehicle Simulator (MAVS) and 32, 64 channel 3D LiDAR point clouds of the RELLIS-3D dataset collected in a real-world off-road environment. Our experimental results demonstrate that sensor and spatial domain shifts significantly impact the performance of LiDAR-based semantic segmentation models. In the absence of spatial domain changes between training and testing, models trained and tested on the same sensor type generally exhibited better performance. Moreover, higher-resolution sensors showed improved performance compared to those with lower-resolution ones. However, results varied when spatial domain changes were present. In some cases, the advantage of a sensor's higher resolution led to better performance both with and without sensor domain shifts. In other instances, the higher resolution resulted in overfitting within a specific domain, causing a lack of generalization capability and decreased performance when tested on data with different sensor configurations.