A pose-graph-based optimization technique is widely used to estimate robot poses using various sensor measurements from devices such as laser scanners and cameras. The global navigation satellite system (GNSS) has recently been used to estimate the absolute 3D position of outdoor mobile robots. However, since the accuracy of GNSS single-point positioning is only a few meters, the GNSS is not used for the loop closure of a pose graph. The main purpose of this study is to generate a loop closure of a pose graph using a time-relative real-time kinematic GNSS (TR-RTK-GNSS) technique. The proposed TR-RTK-GNSS technique uses time differential carrier phase positioning, which is based on carrier-phase-based differential GNSS with a single GNSS receiver. Unlike a conventional RTK-GNSS, we can directly compute the robot's relative position using only a stand-alone GNSS receiver. The initial pose graph is generated from the accumulated velocity computed from GNSS Doppler measurements. To reduce the accumulated error of velocity, we use the TR-RTK-GNSS technique for the loop closure in the graph-based optimization framework. The kinematic positioning tests were performed using an unmanned aerial vehicle to confirm the effectiveness of the proposed technique. From the tests, we can estimate the vehicle's trajectory with approximately 3 cm accuracy using only a stand-alone GNSS receiver.

中文翻译：

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时间相对 RTK-GNSS：姿态图优化中的 GNSS 循环闭合

基于姿势图的优化技术被广泛用于使用来自激光扫描仪和相机等设备的各种传感器测量值来估计机器人姿势。全球导航卫星系统（GNSS）最近被用于估计户外移动机器人的绝对 3D 位置。但是，由于 GNSS 单点定位的精度只有几米，因此 GNSS 不用于位姿图的回环。本研究的主要目的是使用时间相关的实时运动 GNSS (TR-RTK-GNSS) 技术生成位姿图的闭环。提出的 TR-RTK-GNSS 技术使用时间差分载波相位定位，它基于带有单个 GNSS 接收器的基于载波相位的差分 GNSS。与传统的 RTK-GNSS 不同，我们可以直接计算机器人 s 相对位置仅使用独立的 GNSS 接收器。初始位姿图是根据 GNSS 多普勒测量计算的累积速度生成的。为了减少速度的累积误差，我们在基于图的优化框架中使用 TR-RTK-GNSS 技术进行闭环。使用无人机进行运动学定位测试，以确认所提出技术的有效性。从测试中，我们可以仅使用独立的 GNSS 接收器以大约 3 厘米的精度估计车辆的轨迹。使用无人机进行运动学定位测试，以确认所提出技术的有效性。从测试中，我们可以仅使用独立的 GNSS 接收器以大约 3 厘米的精度估计车辆的轨迹。使用无人机进行运动学定位测试，以确认所提出技术的有效性。从测试中，我们可以仅使用独立的 GNSS 接收器以大约 3 厘米的精度估计车辆的轨迹。