4 accepted papers
This letter presents a robust multi-sensor fusion framework for state estimation in legged robots (LLIO) based on an iterated extended Kalman filter. To address the limitations of IMU priori estimation, which often leads to legged robot localization errors or failures, our method integrates the cont…
This letter presents a robust multi-sensor fusion framework for state estimation in legged robots (LLIO) based on an iterated extended Kalman filter. To address the limitations of IMU priori estimation, which often leads to legged robot localization errors or failures, our method integrates the cont
Recent advancements have demonstrated the potential of radiance representations for high-quality mapping and reconstruction. However, these methods face significant challenges in large-scale, multi-agent scenarios, particularly in terms of computational demands and transmission bandwidth requirement
Recently, neural implicit representations have been applied in the mapping process of simultaneous localization and mapping (SLAM), accompanied by less storage overhead and continuous representation. Nevertheless, related methods use a single neural network to represent the whole scene, resulting in