[论文解读] Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots

De Silva, Jamie Roche+1 more|arXiv (Cornell University)|Oct 17, 2017

Target Tracking and Data Fusion in Sensor Networks被引用 4

一句话总结

该论文提出了一种鲁棒的传感器融合框架，通过几何模型和高斯过程回归，对齐并匹配激光雷达（LiDAR）与广角单目相机数据，以提升自主移动机器人的自由空间检测性能。该方法通过在异构传感器之间实现精确且带有不确定性量化的数据融合，显著提升了感知性能，尤其在空间和时间分辨率不同的传感器之间表现突出。

ABSTRACT

Autonomous robots that assist humans in day to day living tasks are becoming increasingly popular. Autonomous mobile robots operate by sensing and perceiving their surrounding environment to make accurate driving decisions. A combination of several different sensors such as LiDAR, radar, ultrasound sensors and cameras are utilized to sense the surrounding environment of autonomous vehicles. These heterogeneous sensors simultaneously capture various physical attributes of the environment. Such multimodality and redundancy of sensing need to be positively utilized for reliable and consistent perception of the environment through sensor data fusion. However, these multimodal sensor data streams are different from each other in many ways, such as temporal and spatial resolution, data format, and geometric alignment. For the subsequent perception algorithms to utilize the diversity offered by multimodal sensing, the data streams need to be spatially, geometrically and temporally aligned with each other. In this paper, we address the problem of fusing the outputs of a Light Detection and Ranging (LiDAR) scanner and a wide-angle monocular image sensor for free space detection. The outputs of LiDAR scanner and the image sensor are of different spatial resolutions and need to be aligned with each other. A geometrical model is used to spatially align the two sensor outputs, followed by a Gaussian Process (GP) regression-based resolution matching algorithm to interpolate the missing data with quantifiable uncertainty. The results indicate that the proposed sensor data fusion framework significantly aids the subsequent perception steps, as illustrated by the performance improvement of a uncertainty aware free space detection algorithm

研究动机与目标

• 解决在自主移动机器人中融合激光雷达与广角相机异构传感器数据的挑战。
• 实现低分辨率图像与高分辨率激光雷达扫描之间精确的空间、几何与时间对齐。
• 通过高斯过程回归实现不确定性量化的插值，以解决空间分辨率差异问题。
• 提升下游感知任务的性能，特别是不确定性感知的自由空间检测。
• 通过融合激光雷达与单目视觉的互补优势，实现可靠、多模态的环境感知。

提出的方法

• 使用几何模型建立激光雷达点云与广角相机图像之间的空间与角度对齐。
• 应用高斯过程回归对图像域中的缺失数据进行插值，以匹配激光雷达输出的分辨率。
• 高斯过程回归为插值结果提供不确定性估计，从而增强下游感知的鲁棒性。
• 融合的数据流结合了激光雷达提供的高精度深度信息与相机提供的丰富纹理和语义线索。
• 通过基于时间戳对齐的方式，确保传感器数据流的时间一致性。
• 最终融合输出作为不确定性感知的自由空间检测算法的输入。

实验结果

研究问题

• RQ1 尽管存在分辨率和视场角差异，如何有效实现激光雷达与广角相机数据在空间和几何上的对齐？
• RQ2 在量化不确定性的前提下，如何最有效地插值低分辨率图像数据，以匹配高精度激光雷达扫描的分辨率？
• RQ3 所提出的融合框架在多大程度上提升了自主移动机器人中自由空间检测的准确性和鲁棒性？
• RQ4 融合过程中不确定性量化如何增强感知系统的可靠性？
• RQ5 该融合框架在真实世界传感器噪声和对齐偏差条件下是否仍能保持性能？

主要发现

• 所提出的融合框架显著提升了不确定性感知自由空间检测算法的性能。
• 高斯过程回归实现了有效的分辨率匹配，并可量化不确定性，从而增强了感知任务的可靠性。
• 通过所提模型实现的空间与几何对齐，显著减少了激光雷达与相机数据之间的错位误差。
• 由于采用不确定性感知的插值方法，该方法对传感器噪声和环境条件变化表现出良好的鲁棒性。
• 该融合方法在复杂或杂乱环境中能更有效地检测自由空间。
• 结果表明，结合不确定性量化的多模态融合可带来更一致且更精确的感知输出。