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LIWO: Lidar-Inertial-Wheel Odometry (2302.14298v2)

Published 28 Feb 2023 in cs.RO

Abstract: LiDAR-inertial odometry (LIO), which fuses complementary information of a LiDAR and an Inertial Measurement Unit (IMU), is an attractive solution for state estimation. In LIO, both pose and velocity are regarded as state variables that need to be solved. However, the widely-used Iterative Closest Point (ICP) algorithm can only provide constraint for pose, while the velocity can only be constrained by IMU pre-integration. As a result, the velocity estimates inclined to be updated accordingly with the pose results. In this paper, we propose LIWO, an accurate and robust LiDAR-inertialwheel (LIW) odometry, which fuses the measurements from LiDAR, IMU and wheel encoder in a bundle adjustment (BA) based optimization framework. The involvement of a wheel encoder could provide velocity measurement as an important observation, which assists LIO to provide a more accurate state prediction. In addition, constraining the velocity variable by the observation from wheel encoder in optimization can further improve the accuracy of state estimation. Experiment results on two public datasets demonstrate that our system outperforms all state-of-the-art LIO systems in terms of smaller absolute trajectory error (ATE), and embedding a wheel encoder can greatly improve the performance of LIO based on the BA framework.

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Citations (4)
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Summary

  • The paper introduces LIWO, a novel odometry system that integrates LiDAR, IMU, and wheel encoder data via a tightly coupled bundle adjustment framework.
  • It achieves superior absolute trajectory error performance on datasets like nclt and kaist compared to conventional LiDAR-IMU odometry systems.
  • The method enhances robustness in state estimation for autonomous vehicles and paves the way for advanced sensor fusion techniques in mobile robotics.

Overview of LIWO: Lidar-Inertial-Wheel Odometry

The paper introduces LIWO, a novel odometry system that integrates measurements from LiDAR, an Inertial Measurement Unit (IMU), and a wheel encoder using a bundle adjustment (BA) based optimization framework. This integration is designed to provide an advanced approach for precise and robust state estimation in robotics, particularly for applications involving unmanned vehicles and autonomous navigation.

Technical Summary

LiDAR-inertial odometry (LIO) has conventionally relied on the fusion of LiDAR and IMU data. However, these systems often face challenges because the Iterative Closest Point (ICP) algorithm typically used in these systems provides constraints only for pose estimation, leaving velocity updates dependent on IMU pre-integration. This limitation can result in inaccuracies, particularly when the IMU pre-integrations are not adequately reliable.

LIWO addresses these challenges by adding a wheel encoder to the conventional LiDAR-IMU setup. The wheel encoder provides direct velocity measurements, which are incorporated as observations within the optimization framework, thereby supplementing the existing constraints and improving the overall accuracy of the state predictions. The optimization problem is modeled in a tightly coupled BA framework, integrating pose, velocity, and sensor biases as state variables. The framework also uses pre-integrated IMU measurements and wheel encoder readings to refine these variables iteratively.

Results and Implications

Experimentation on public datasets, including the comprehensive ncltnclt and kaistkaist datasets, demonstrates that LIWO achieves superior performance in absolute trajectory error (ATE) compared to state-of-the-art LIO systems. The results highlight the robustness and accuracy gains achieved by incorporating wheel encoder data into the odometry process.

The paper suggests significant implications for the field of robotics and autonomous vehicles. By integrating wheel encoder data, which is typically underutilized, LIWO presents a more accurate solution for environments where traditional LIO might struggle, such as complex or rapidly changing landscapes. The system's compatibility with 6-axis IMUs enhances its practicality across different hardware platforms, without necessitating additional costly equipment like the Attitude and Heading Reference System (AHRS).

Future Prospects

Looking toward future developments, LIWO opens avenues for improving sensor fusion techniques in mobile robotics. The current model can be further refined to address potential discrepancies such as wheel slippage or speed inconsistencies between left and right wheels during turns. These improvements could enhance the applicability of LIWO in diverse real-world scenarios, including urban navigation and off-road exploration.

Furthermore, the paper sets a foundation for ongoing research into more seamless integration of diverse sensors for state estimation, encouraging the exploration of novel algorithms and optimization techniques that can handle complex sensor data interactions more efficiently.

In conclusion, LIWO represents an advancement in odometry systems, contributing significantly to the accuracy and reliability of state estimation in autonomous systems by leveraging an effective combination of multiple sensory inputs in a structured optimization context. Its open-sourced nature promises to accelerate development and collaborative enhancements within the robotics community.

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