对比两款激光惯性里程计在真实花园环境中的表现
Performance Assessment of Lidar Odometry Frameworks: A Case Study at the Australian Botanic Garden Mount Annan
- 用128线激光雷达+GPS/IMU采集真实花园数据，评估算法性能
- COIN-LIO水平方向误差更小，长距离轨迹更稳定
- 适合自动驾驶在复杂自然场景下定位算法选型参考
自动驾驶车辆正在全球多样环境中测试，但缺乏对森林或花园等自然非结构化环境的数据集评估。为此，本文在澳大利亚蒙安南植物园开展定位研究，该区域包含开阔草坪、铺装步道及密集植被区。数据由128线激光雷达与GPS/IMU同步采集，用于追踪车辆。本研究评估了两种前沿激光惯性里程计框架：COIN-LIO与LIO-SAM。分析了轨迹在水平与垂直维度的估计表现，以及不同距离下的相对平移和航向角误差。结果表明，尽管两者在垂直平面表现良好，但COIN-LIO在水平方向更具精度优势，尤其在长距离轨迹中；而LIO-SAM随距离增加出现明显漂移与航向误差。
原文摘要 · Abstract (English)
Autonomous vehicles are being tested in diverse environments worldwide. However, a notable gap exists in evaluating datasets representing natural, unstructured environments such as forests or gardens. To address this, we present a study on localisation at the Australian Botanic Garden Mount Annan. This area encompasses open grassy areas, paved pathways, and densely vegetated sections with trees and other objects. The dataset was recorded using a 128-beam LiDAR sensor and GPS and IMU readings to track the ego-vehicle. This paper evaluates the performance of two state-of-the-art LiDARinertial odometry frameworks, COIN-LIO and LIO-SAM, on this dataset. We analyse trajectory estimates in both horizontal and vertical dimensions and assess relative translation and yaw errors over varying distances. Our findings reveal that while both frameworks perform adequately in the vertical plane, COINLIO demonstrates superior accuracy in the horizontal plane, particularly over extended trajectories. In contrast, LIO-SAM shows increased drift and yaw errors over longer distances.
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