4 accepted papers
Multi-modal sensor fusion in Bird's Eye View (BEV) representation has become the leading approach for 3D object detection. However, existing methods often rely on depth estimators or transformer encoders to transform image features into BEV space, which reduces robustness or introduces significant c…
In this study, we address the off-road traversability estimation problem, that predicts areas where a robot can navigate in off-road environments. An off-road environment is an unstructured environment comprising a combination of traversable and non-traversable spaces, which presents a challenge for…
We propose a point-cloud-centric depth completion method called attention bilateral convolutional network for depth completion (ABCD). The proposed method uses LiDAR data and camera data to improve the resolution of the sparse depth information. Color images, which have been seen as fundamental to d
We present an accurate and robust image-to-point cloud registration method that is viable in urban and off-road environments. Existing image-to-point cloud registration methods have focused on vehicle platforms along paved roads. Therefore, image-to-point cloud registration on UGV platforms for off-