面向地面机器人的松耦合4D雷达-惯性里程计
Loosely coupled 4D-Radar-Inertial Odometry for Ground Robots
AI总结：
针对地面机器人仅用4D雷达与IMU的里程计问题，提出滑动窗口图优化及自速度估计改进，在NTU4DRadLM多数轨迹上提升最先进性能。
摘要：
精确的机器人里程计对于自主导航至关重要。尽管已有大量基于各类传感器套件开发的技术，但仅使用雷达和IMU进行里程计估计仍是一个研究不足的领域。在相机或LiDAR等传统传感器可能表现不佳的环境中，尤其是在弱光条件下或面临雾、雨、烟雾等环境挑战时，雷达显得尤为有价值。然而，尽管雷达具有鲁棒性，其数据噪声更大且更容易出现异常值，因此需要专门的处理方法。本文提出一种基于图优化并采用滑动窗口的雷达里程计方法，旨在通过构建连接网络来保持位姿之间的鲁棒关系，同时使计算成本保持固定（这对长轨迹尤其有益）。此外，我们针对全向和非全向地面车辆引入了自速度估计方面的改进，从而改善优化器所需的直接里程计输入。最后，我们将所提方法与现有算法进行了对比研究，结果表明，我们的纯里程计方法在NTU4DRadLM数据集的大多数轨迹上提升了现有最先进水平，并在关键性能指标评估中取得了良好结果。
英文摘要：
Accurate robot odometry is essential for autonomous navigation. While numerous techniques have been developed based on various sensor suites, odometry estimation using only radar and IMU remains an underexplored area. Radar proves particularly valuable in environments where traditional sensors, like cameras or LiDAR, may struggle, especially in low-light conditions or when faced with environmental challenges like fog, rain or smoke. However, despite its robustness, radar data is noisier and more prone to outliers, requiring specialized processing approaches. In this paper, we propose a graph-based optimization approach using a sliding window for radar-based odometry, designed to maintain robust relationships between poses by forming a network of connections, while keeping computational costs fixed (specially beneficial in long trajectories). Additionally, we introduce an enhancement in the ego-velocity estimation specifically for ground vehicles, both holonomic and non-holonomic, which subsequently improves the direct odometry input required by the optimizer. Finally, we present a comparative study of our approach against existing algorithms, showing how our pure odometry approach inproves the state of art in most trajectories of the NTU4DRadLM dataset, achieving promising results when evaluating key performance metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
