RESPLE: Recursive Spline Estimation for LiDAR-Based Odometry (2504.11580v2)

Abstract: We present a novel recursive Bayesian estimation framework using B-splines for continuous-time 6-DoF dynamic motion estimation. The state vector consists of a recurrent set of position control points and orientation control point increments, enabling efficient estimation via a modified iterated extended Kalman filter without involving error-state formulations. The resulting recursive spline estimator (RESPLE) is further leveraged to develop a versatile suite of direct LiDAR-based odometry solutions, supporting the integration of one or multiple LiDARs and an IMU. We conduct extensive real-world evaluations using public datasets and our own experiments, covering diverse sensor setups, platforms, and environments. Compared to existing systems, RESPLE achieves comparable or superior estimation accuracy and robustness, while attaining real-time efficiency. Our results and analysis demonstrate RESPLE's strength in handling highly dynamic motions and complex scenes within a lightweight and flexible design, showing strong potential as a universal framework for multi-sensor motion estimation. We release the source code and experimental datasets at https://github.com/ASIG-X/RESPLE.

Recursive Spline Estimation for LiDAR-Based Odometry

The paper "RESPLE: Recursive Spline Estimation for LiDAR-Based Odometry" presents an innovative approach to six degrees of freedom (6-DoF) dynamic motion estimation by implementing recursive Bayesian estimation frameworks using B-splines. The focus is on enhancing the precision and robustness in motion estimation through a novel recursive spline estimator that efficiently integrates LiDAR and Inertial Measurement Unit (IMU) inputs.

Technical Contribution

The authors introduce the Recursive Spline Estimator (RESPLE), which relies on recursive B-spline estimation techniques to address 6-DoF motion estimation challenges without utilizing error-state formulations. A distinct feature of RESPLE is its separation of position and orientation components in the motion representation, utilizing recurrent control points (RCPs) for position alongside quaternion increments for orientation. This separation reduces nonlinearity and allows for more computational efficiency.

Evaluation and Results

The paper provides a comprehensive evaluation of RESPLE across multiple datasets, including NTU VIRAL, MCD, and GrandTour, along with own experimental setups such as HelmDyn. It claims superior performance over existing state-of-the-art odometry solutions such as KISS-ICP, Traj-LO, CTE-MLO, FAST-LIO2, Point-LIO, and SLICT2. Specifically, RESPLE’s variants showed improved robustness and accuracy, particularly in scenarios where LiDAR provides non-degenerate sensing data. Notably, RESPLE’s LiDAR-only variant performed comparably to LiDAR-inertial systems in most scenarios without significant LiDAR degeneracy, emphasizing the effectiveness of the proposed spline-based recursive estimation process.

Implications and Future Work

The implications of this research extend to mobile robotics applications requiring precise and real-time motion estimation. By avoiding complex error-state formulations and selectively incorporating sensor data, RESPLE improves computational efficiency and estimation accuracy. This advancement has practical applications in autonomous vehicles, robotic navigation, and wearable technology, enhancing their ability to function effectively in dynamic and varied environments.

Moving forward, the authors will explore integrating visual sensors to manage LiDAR degeneracy better and enhancing the framework with a back-end for global correction. Furthermore, expanding RESPLE's application to downstream tasks like motion planning can leverage its trajectory uncertainties for improved decision-making and autonomous navigational strategies.

In conclusion, the paper offers significant contributions by proposing a robust, recursive approach to dynamic motion estimation using B-splines, demonstrating its superiority over contemporary methods in extensive real-world benchmarks. The release of the source code further supports its adoption and evaluation within the research community.