3 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
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
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