6 accepted papers
This work proposes a semi-elastic optimizationbased LiDAR-inertial state estimation method, which balances the constraints from LiDAR, IMU and consistency according to their unique characteristics, thereby imparts appropriate elasticity for current state to be optimized to the correct value, and ens
In visual-inertial odometry (VIO) systems, allocating limited computational resources to constraints for new frames is more conducive to enhancing the accuracy of state estimation, as old frames have already undergone multiple updates. To enable VIO to efficiently index the observed map points of ne
A complete SLAM system comprises a front-end odometry module and a back-end optimization module. The front-end utilizes sensor data (such as from cameras or LiDAR) to estimate the robot's pose and construct a map of the surrounding environment. Meanwhile, the task of the back-end is to determine whe
This paper proposes a novel LiDAR-Inertial odometry (LIO), named SR-LIO, based on an error state iterated Kalman filter (ESIKF) framework. We adapt the sweep reconstruction method, which segments and reconstructs raw input sweeps from spinning LiDAR to obtain reconstructed sweeps with higher frequen…
Existing LiDAR-inertial-visual odometry and mapping (LIV-OAM) systems mainly utilize the LiDAR-inertial odometry (LIO) module for structure reconstruction and the LiDAR-assisted visual-inertial odometry (VIO) module for color rendering. However, the performance of existing LiDAR-assisted VIO module
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 Po…