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.

翻译：自动驾驶车辆与轮式机器人已广泛应用于室内外多种场景。在GNSS信号受限或光照条件不佳的实际环境中，导航解算可能仅依赖惯性传感器，并因惯性测量误差而产生随时间累积的漂移。本研究提出WiCHINS系统——一种通过融合轮载惯性传感器与底盘惯性传感器的轮式底盘惯性导航系统，以实现精确的纯惯性导航。为此，我们推导出包含三个专用扩展卡尔曼滤波器的三阶段框架，该框架在估计过程中充分利用轮体与车体不同安装位置的优势。为评估所提方法，我们采用包含五个惯性测量单元、总记录时长达228.6分钟的数据集进行验证。通过对比四种惯性基准方法，实验表明使用两个轮载与一个车体惯性测量单元时，平均位置误差为11.4米，相当于平均行进距离的$2.4\%$。因此，所提方法能够在挑战环境中实现鲁棒导航，有效弥合纯惯性导航的性能差距。