This paper presents a method that leverages vehicle motion constraints to refine data associations in a point-based radar odometry system. By using the strong prior on how a non-holonomic robot is constrained to move smoothly through its environment, we develop the necessary framework to estimate ego-motion from a single landmark association rather than considering all of these correspondences at once. This allows for informed outlier detection of poor matches that are a dominant source of pose estimate error. By refining the subset of matched landmarks, we see an absolute decrease of 2.15% (from 4.68% to 2.53%) in translational error, approximately halving the error in odometry (reducing by 45.94%) than when using the full set of correspondences. This contribution is relevant to other point-based odometry implementations that rely on a range sensor and provides a lightweight and interpretable means of incorporating vehicle dynamics for ego-motion estimation.
翻译:本文展示了一种方法,利用车辆运动限制来完善基于点的雷达测量系统的数据联系。 通过使用非光学机器人如何在环境中顺利移动的强力前程,我们开发了必要的框架来估计单一里程碑式协会的自我提升,而不是同时考虑所有这些对应。 这使得能够对作为造成估计误差的主要来源的差错进行知情的外部检测。 通过精炼匹配的标志子组,我们看到翻译误差绝对减少了2.15%(从4.68%降至2.53%),大约将odologic误差(减少45.94%)减半,而不是使用全套通信。 这一贡献与其他基于点的ododograph措施的实施相关,这些应用依赖于一个测距传感器,提供了将车辆动态纳入自我动作估计的轻量和可解释手段。