In this paper, we propose a method for online estimation of the robot's posture. Our method uses von Mises and Bingham distributions as probability distributions of joint angles and 3D orientation, which are used in directional statistics. We constructed a particle filter using these distributions and configured a system to estimate the robot's posture from various sensor information (e.g., joint encoders, IMU sensors, and cameras). Furthermore, unlike tangent space approximations, these distributions can handle global features and represent sensor characteristics as observation noises. As an application, we show that the yaw drift of a 6-axis IMU sensor can be represented probabilistically to prevent adverse effects on attitude estimation. For the estimation, we used an approximate model that assumes the actual robot posture can be reproduced by correcting the joint angles of a rigid body model. In the experiment part, we tested the estimator's effectiveness by examining that the joint angles generated with the approximate model can be estimated using the link pose of the same model. We then applied the estimator to the actual robot and confirmed that the gripper position could be estimated, thereby verifying the validity of the approximate model in our situation.

翻译：本文提出了一种机器人姿态在线估计方法。该方法采用方向统计学中用于处理关节角度和三维方向的冯·米塞斯分布与宾厄姆分布，构建以这些分布为基础的粒子滤波器，并配置了能够从多种传感器信息（如关节编码器、惯性测量单元和相机）中估计机器人姿态的系统。与切空间近似方法不同，这些分布能处理全局特征，并将传感器特性表征为观测噪声。作为应用实例，我们展示了如何通过概率化表示六轴惯性测量单元的偏航漂移，从而避免对姿态估计产生不利影响。在估计过程中，我们采用了一个近似模型，该模型假设通过修正刚体模型的关节角度即可复现实际机器人姿态。实验部分，我们通过检验该模型生成的关节角度能否利用同一模型的连杆位姿进行估计，验证了估计器的有效性。随后将估计器应用于实际机器人，确认了夹爪位置的可估计性，从而在实验场景下验证了近似模型的合理性。