10 accepted papers
Accurate state estimation is essential for an autonomous agricultural robot’s reliable operations. The effectiveness of state estimation is influenced by a number of factors, such as sensor-fusion algorithms, the environment, and sensor quality. When the robot traverses in large-scale scenarios, the…
This paper presents a new tightly coupled LiDAR-inertial odometry (LIO) scheme for a quadruped robot operating under fluctuating conditions. By proposing external disturbance modeling, the profile of unknown disturbances and noise on the IMU is effectively characterized using the time delay estimati
This work presents a FusionGS-SLAM, a robust framework for simultaneous localization and real-time photorealistic mapping leveraging the power of sensor fusion techniques. To achieve this, the proposed method employs a tightly-coupled technique to effectively combine multiple factors from improved s…
This paper presents a new visual localization framework for complex indoor environments under dynamic scene change conditions. Conventional visual localization methods often struggle to maintain accuracy and robustness in such environments, where frequent scene changes, occlusions, diverse object ca
This paper presents a new visual localization framework for complex indoor environments under dynamic scene change conditions. Conventional visual localization methods often struggle to maintain accuracy and robustness in such environments, where frequent scene changes, occlusions, diverse object ca…
Visual--inertial SLAM systems often fail in feature-poor environments such as corridors and textureless walls, leading to catastrophic tracking loss. Existing methods detect degradation reactively after failure occurs, leaving little opportunity for corrective action. We propose a proactive framewor…
This work presents a tightly coupled LiDAR–inertial odometry (LIO) framework tailored for quadruped robots operating under vibration and fluctuating conditions. By integrating time delay estimation (TDE) into an error-state Kalman filter (ESKF), external disturbances affecting the IMU are explicitly…
Accurate state estimation is essential for an autonomous agricultural robot's reliable operations. The effectiveness of state estimation is influenced by a number of factors, such as sensor-fusion algorithms, the environment, and sensor quality. When the robot traverses in large-scale scenarios, the
This work presents a FusionGS-SLAM, a robust framework for simultaneous localization and real-time photorealistic mapping leveraging the power of sensor fusion techniques. To achieve this, the proposed method employs a tightly-coupled technique to effectively combine multiple factors from improved s
This letter presents a new tightly coupled LiDAR-visual-inertial odometry scheme for agricultural autonomous machinery under a structureless environment and the presence of fluctuation uncertainties. By proposing the robust adaptive filter, the effects of unknown disturbances and noises are signific