DUDMap: 3D RGB-D mapping for dense, unstructured, and dynamic environment

Dynamic RGB-D Mapping

Localization and mapping has been an area of great importance and interest to the robotics and computer vision community. It has traditionally been accomplished with range sensors such as lasers and sonars. Recent improvements in processing power coupled with advancements in image matching and motion estimation has allowed development of vision based localization techniques. Despite much progre...

RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments

RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 3D mapping system tha...

RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments

RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. RGB-D cameras rely on either structured light patterns combined with stereo sensing [6,10] or time-of-flight laser sensing [1] to generate depth estimates that can be associated with RGB pixels. Very soon, small, high-quality RGB-D cameras developed for computer gaming and home entertainment ...

Dense RGB-D semantic mapping with Pixel-Voxel neural network

For intelligent robotics applications, extending 3D mapping to 3D semantic mapping enables robots to, not only localize themselves with respect to the scene’s geometrical features but also simultaneously understand the higher level meaning of the scene contexts. Most previous methods focus on geometric 3D reconstruction and scene understanding independently notwithstanding the fact that joint e...

An Adaptative Registration of Rgb-d Data for 3d Indoor Environment Mapping