融合轮上与车身惯性传感器，实现高精度纯惯性导航。
Pure Inertial Navigation in Challenging Environments with Wheeled and Chassis Mounted Inertial Sensors
- 分三阶段用扩展卡尔曼滤波，结合轮子与车身传感器优势。
- 仅用两个轮子和一个车身传感器，定位误差仅11.4米（总路程2.4%）。
- 适合无卫星信号或光线差的复杂环境，如室内或隧道。
自主车辆和轮式机器人在室内外广泛应用。在GNSS信号受限或光照条件差的情况下，导航系统只能依赖惯性传感器，导致随时间产生漂移。本文提出WiCHINS，一种融合轮装与车身惯性传感器的纯惯性导航系统。设计了三阶段框架，每阶段使用专用扩展卡尔曼滤波器，充分利用不同安装位置的优势。通过包含五个惯性测量单元、总时长228.6分钟的数据集评估，与四种基线方法对比，仅使用两个轮子和一个车身传感器时，平均定位误差为11.4米，约为总行驶距离的2.4%。结果表明，该方法能显著提升复杂环境下的鲁棒性导航能力，有效缩小纯惯性导航性能差距。
原文摘要 · Abstract (English)
Autonomous vehicles and wheeled robots are widely used in many applications in both indoor and outdoor settings. In practical situations with limited GNSS signals or degraded lighting conditions, the navigation solution may rely only on inertial sensors and as result drift in time due to errors in the inertial measurement. In this work, we propose WiCHINS, a wheeled and chassis inertial navigation system by combining wheel-mounted-inertial sensors with a chassis-mounted inertial sensor for accurate pure inertial navigation. To that end, we derive a three-stage framework, each with a dedicated extended Kalman filter. This framework utilizes the benefits of each location (wheel/body) during the estimation process. To evaluate our proposed approach, we employed a dataset with five inertial measurement units with a total recording time of 228.6 minutes. We compare our approach with four other inertial baselines and demonstrate an average position error of 11.4m, which is $2.4\%$ of the average traveled distance, using two wheels and one body inertial measurement units. As a consequence, our proposed method enables robust navigation in challenging environments and helps bridge the pure-inertial performance gap.
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