Wheel-SLAM: Simultaneous Localization and Terrain Mapping Using One Wheel-mounted IMU (2211.03174v2)

Abstract: A reliable pose estimator robust to environmental disturbances is desirable for mobile robots. To this end, inertial measurement units (IMUs) play an important role because they can perceive the full motion state of the vehicle independently. However, it suffers from accumulative error due to inherent noise and bias instability, especially for low-cost sensors. In our previous studies on Wheel-INS \cite{niu2021, wu2021}, we proposed to limit the error drift of the pure inertial navigation system (INS) by mounting an IMU to the wheel of the robot to take advantage of rotation modulation. However, Wheel-INS still drifted over a long period of time due to the lack of external correction signals. In this letter, we propose to exploit the environmental perception ability of Wheel-INS to achieve simultaneous localization and mapping (SLAM) with only one IMU. To be specific, we use the road bank angles (mirrored by the robot roll angles estimated by Wheel-INS) as terrain features to enable the loop closure with a Rao-Blackwellized particle filter. The road bank angle is sampled and stored according to the robot position in the grid maps maintained by the particles. The weights of the particles are updated according to the difference between the currently estimated roll sequence and the terrain map. Field experiments suggest the feasibility of the idea to perform SLAM in Wheel-INS using the robot roll angle estimates. In addition, the positioning accuracy is improved significantly (more than 30\%) over Wheel-INS. The source code of our implementation is publicly available (https://github.com/i2Nav-WHU/Wheel-SLAM).

• The paper presents a novel SLAM approach using a single wheel-mounted IMU to capture roll angles for detecting terrain features.
• It integrates a Rao-Blackwellized particle filter with loop closure detection via roll angle sequence matching to refine state updates.
• Experimental results demonstrate over 30% improvement in positioning and heading accuracy, offering a cost-effective solution for drift control.

Analysis of "Wheel-SLAM: Simultaneous Localization and Terrain Mapping Using One Wheel-mounted IMU"

The paper "Wheel-SLAM: Simultaneous Localization and Terrain Mapping Using One Wheel-mounted IMU" presents a novel approach to Simultaneous Localization and Mapping (SLAM) using an inertial measurement unit (IMU) mounted on the wheel of a robot. This method, termed Wheel-SLAM, aims to offer robust localization and terrain mapping by leveraging the distinct advantages of wheel-mounted sensors, primarily focusing on the roll angle as an indicator of the terrain features. This work extends the previous Wheel-INS framework by integrating loop closure detection using terrain features.

Key Contributions

1. Unique Utilization of IMU for SLAM: The paper presents the innovative use of a single wheel-mounted IMU for SLAM applications. Unlike traditional setups requiring multiple sensors, Wheel-SLAM harnesses the unconventionally mounted IMU to exploit terrain features for mapping, focusing on road bank angles derived from roll angle estimations.
2. Integration with Particle Filter: The paper employs a Rao-Blackwellized particle filter framework to accurately sample and update the robot state and terrain map. Each particle represents a potential robot state and corresponding terrain feature map, with loop closure detection refined through roll angle sequence matching.
3. Enhanced Positioning Performance: Experimental results show that Wheel-SLAM improves positioning and heading accuracy by over 30% compared to the predecessor Wheel-INS, particularly by effectively constraining long-term positional drift through loop closure mechanisms leveraging terrain features.
4. Public Availability: The implementation of Wheel-SLAM has been made publicly accessible, advocating transparency and encouraging further research and development in the domain.

Numerical Results and Experimental Validation

The research validates Wheel-SLAM's feasibility through extensive real-world experiments conducted with a vehicle equipped with the developed system. The results demonstrate significant reductions in cumulative error, surpassing traditional inertial navigation systems (INS) in terms of drift control. The experiments revealed improvements in the root mean square error (RMSE) for both horizontal position and heading, achieving robust localization even in complex environments.

Implications and Future Work

Practical Implications: The practical implications of Wheel-SLAM primarily stem from its cost-effectiveness and simplicity, enabling efficient localization in environments with moderate complexity without the need for high-cost sensor suites like LiDAR or multiple cameras. This could be particularly beneficial for low-budget applications or environments where such equipment's deployment is challenging.

Theoretical Advances: The integration of inertial sensing for loop closure in SLAM contributes to expanding the understanding and application of IMUs in robotic navigation, emphasizing the potential of low-cost sensors to perform tasks traditionally managed by more expensive systems.

Speculation on Future Developments: Looking forward, future enhancements could integrate additional sensors to mitigate the limitations observed with homogeneous flat terrains that challenge the detection of loop closures. Furthermore, the development of hybrid systems could allow for more robust loop closure strategies by combining inertial data with visual inputs, offering a more comprehensive SLAM solution.

Conclusion

Overall, "Wheel-SLAM" provides a significant step forward in SLAM systems oriented towards robots with repetitive routes and constrained operation environments. By innovatively utilizing a single wheel-mounted IMU, the approach not only offers an economic solution but also opens new avenues for research in IMU-based localization systems, warranting further exploration in diverse applications beyond the tested scenarios.