Accurate perception of dynamic obstacles is essential for autonomous robot navigation in indoor environments. Although sophisticated 3D object detection and tracking methods have been investigated and developed thoroughly in the fields of computer vision and autonomous driving, their demands on expensive and high-accuracy sensor setups and substantial computational resources from large neural networks make them unsuitable for indoor robotics. Recently, more lightweight perception algorithms leveraging onboard cameras or LiDAR sensors have emerged as promising alternatives. However, relying on a single sensor poses significant limitations: cameras have limited fields of view and can suffer from high noise, whereas LiDAR sensors operate at lower frequencies and lack the richness of visual features. To address this limitation, we propose a dynamic obstacle detection and tracking framework that uses both onboard camera and LiDAR data to enable lightweight and accurate perception. Our proposed method expands on our previous ensemble detection approach, which integrates outputs from multiple low-accuracy but computationally efficient detectors to ensure real-time performance on the onboard computer. In this work, we propose a more robust fusion strategy that integrates both LiDAR and visual data to enhance detection accuracy further. We then utilize a tracking module that adopts feature-based object association and the Kalman filter to track and estimate detected obstacles' states. Besides, a dynamic obstacle classification algorithm is designed to robustly identify moving objects. The dataset evaluation demonstrates a better perception performance compared to benchmark methods. The physical experiments on a quadcopter robot confirms the feasibility for real-world navigation.

翻译：在室内环境中，准确感知动态障碍物对于自主机器人导航至关重要。尽管计算机视觉和自动驾驶领域已深入研究和开发了复杂的三维物体检测与跟踪方法，但这些方法对昂贵高精度传感器配置的需求，以及大型神经网络对大量计算资源的消耗，使其不适用于室内机器人领域。近年来，利用机载摄像头或激光雷达传感器的更轻量级感知算法已成为有前景的替代方案。然而，依赖单一传感器存在显著局限：摄像头视场有限且易受高噪声干扰，而激光雷达传感器工作频率较低且缺乏丰富的视觉特征。为克服此局限，我们提出一种动态障碍物检测与跟踪框架，该框架同时利用机载摄像头与激光雷达数据，以实现轻量且精确的感知。我们所提方法基于先前的集成检测方案进行扩展，该方案通过整合多个低精度但计算高效的检测器输出，确保在机载计算机上实现实时性能。在本工作中，我们提出一种更鲁棒的融合策略，通过整合激光雷达与视觉数据以进一步提升检测精度。随后，我们采用基于特征的物体关联方法与卡尔曼滤波器的跟踪模块，对检测到的障碍物状态进行跟踪与估计。此外，本文设计了一种动态障碍物分类算法，用于鲁棒地识别运动物体。数据集评估表明，与基准方法相比，本方法具有更优的感知性能。在四旋翼机器人上的物理实验验证了其在真实世界导航中的可行性。