仅使用单个相机在野外环境中跟踪蛇形机器人
Tracking Snake-like Robots in the Wild Using Only a Single Camera
AI总结：
本文提出结合可微渲染与 Kalman filter 的单相机无标记跟踪方法，可在静止或移动相机下同时估计蛇形机器人位姿、关节角及不确定性。
摘要：
机器人在复杂环境中的导航需要精确的状态估计和定位，以确保稳健且安全的运行。对于像机器蛇这样的移动机器人，传统的感知方法需要多个嵌入式传感器或标记，从而导致复杂性、成本以及故障点增加。另一种方案是在环境中部署外部相机，这种做法非常容易实现，而根据该相机图像对机器人进行无标记状态估计是一种理想的解决方案：既简单又具有成本效益。然而，这一过程中的挑战在于在更大的环境中跟踪机器人，此时相机可能在没有外参标定的情况下被移动，或者相机本身可能处于运动状态（例如，跟随机器人的无人机）。该场景本身呈现出一个复杂挑战：在含噪声观测下根据单幅图像重建机器人位姿。本文解决了从单个相机跟踪行走式移动机器人的问题。该方法将可微渲染与 Kalman filter（卡尔曼滤波）相结合。这种协同作用使得机器人关节角度和位姿能够被同时估计，同时还提供状态不确定性，该不确定性随后可用于鲁棒控制。我们在蛇形机器人上、在静止和非静止（移动）相机两种条件下验证了方法的有效性，并在结构化和非结构化场景中确认了其性能。所得结果表明，机器人基座位置定位的平均误差为 0.05 m，关节状态估计的平均误差为 6 度。我们相信，这项新技术为未来探索和搜救任务中增强机器人机动性与导航能力开辟了可能性。
英文摘要：
Robot navigation within complex environments requires precise state estimation and localization to ensure robust and safe operations. For ambulating mobile robots like robot snakes, traditional methods for sensing require multiple embedded sensors or markers, leading to increased complexity, cost, and increased points of failure. Alternatively, deploying an external camera in the environment is very easy to do, and marker-less state estimation of the robot from this camera's images is an ideal solution: both simple and cost-effective. However, the challenge in this process is in tracking the robot under larger environments where the cameras may be moved around without extrinsic calibration, or maybe when in motion (e.g., a drone following the robot). The scenario itself presents a complex challenge: single-image reconstruction of robot poses under noisy observations. In this paper, we address the problem of tracking ambulatory mobile robots from a single camera. The method combines differentiable rendering with the Kalman filter. This synergy allows for simultaneous estimation of the robot's joint angle and pose while also providing state uncertainty which could be used later on for robust control. We demonstrate the efficacy of our approach on a snake-like robot in both stationary and non-stationary (moving) cameras, validating its performance in both structured and unstructured scenarios. The results achieved show an average error of 0.05 m in localizing the robot's base position and 6 degrees in joint state estimation. We believe this novel technique opens up possibilities for enhanced robot mobility and navigation in future exploratory and search-and-rescue missions.
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。
