AC-LIO: Towards Asymptotic and Consistent Convergence in LiDAR-Inertial Odometry
Tianxiang Zhang
1,3
Xuanxuan Zhang
1
,*
Wenlei Fan
1
Xin Xia
2
Huai Yu
1
Lin Wang
3
You Li
1
1 Wuhan University
,
2 University of Michigan-Dearborn
,
3 Nanyang Technological University
(
* : Corresponding Author
)
Abstract
🔸Existing LiDAR-Inertial Odometry (LIO) methods typically utilize the prior state trajectory derived from the IMU integration to compensate for the motion distortion within LiDAR frames. However, discrepancies between the prior and actual trajectory can lead to residual distortions that compromise the consistency of the LiDAR frame with its corresponding geometric environment. This imbalance may result in pointcloud registration becoming trapped in local optima, thereby exacerbating drift during long-term and large-scale localization.
🔸To address the issue, we propose a novel asymptotically and consistently converging LIO framework dubbed AC-LIO. Our key idea is to back propagate current update term based on the prior state chain, and asymptotically compensate for the residual distortion during iteration. Moreover, considering the weak correlation between previous error and current distortion, we establish convergence criteria based on the pointcloud constraints to regulate the backpropagation.This method of guiding asymptotic distortion compensation using convergence criteria subtly enhances the consistent convergence of pointcloud registration, futher improving the accuracy and robustness of LIO system.
🔸Extensive experiments demonstrate that our AC-LIO framework significantly promotes consistent convergence in state estimation compared to prior arts, with about 30.4% reduction in average RMSE over the second best result, leading to marked improvements in the accuracy of long-term and large-scale localization and mapping.
🔸To address the issue, we propose a novel asymptotically and consistently converging LIO framework dubbed AC-LIO. Our key idea is to back propagate current update term based on the prior state chain, and asymptotically compensate for the residual distortion during iteration. Moreover, considering the weak correlation between previous error and current distortion, we establish convergence criteria based on the pointcloud constraints to regulate the backpropagation.This method of guiding asymptotic distortion compensation using convergence criteria subtly enhances the consistent convergence of pointcloud registration, futher improving the accuracy and robustness of LIO system.
🔸Extensive experiments demonstrate that our AC-LIO framework significantly promotes consistent convergence in state estimation compared to prior arts, with about 30.4% reduction in average RMSE over the second best result, leading to marked improvements in the accuracy of long-term and large-scale localization and mapping.
(I) Accuracy and Criteria Validity Evaluation
(II) Principal Comparison
(a) A principal comparison of our AC-LIO with traditional approaches. Conventional LIO typically considers an initial distortion compensation via IMU, yet the residual distortion may prevent the registration from further convergence. In contrast, our AC-LIO employs an asymptotic backpropagation to promote consistent convergence in state estimation.
(b) Large scale localization and mapping result of BotanicGarden dataset. We achieve better accuracy performance than other benchmark frameworks on its sequence 1008_01 with VLP16 LiDAR.
(b) Large scale localization and mapping result of BotanicGarden dataset. We achieve better accuracy performance than other benchmark frameworks on its sequence 1008_01 with VLP16 LiDAR.
(III) System Overview
(a) The system overview of our AC-LIO framework. During iterative ESKF, asymptotic compensation of pointcloud distortion is conducted based on the convergence criteria, to promote consistent convergence of state estimation.
(b) The on-manifold propagation. The blue shows the forward propagation of IMU integration. The green indicates the backward recursion of the update term according to the propagation matrix and noise relation.
(b) The on-manifold propagation. The blue shows the forward propagation of IMU integration. The green indicates the backward recursion of the update term according to the propagation matrix and noise relation.
