Interaction between a robot and its environment requires perception about the environment,
which helps the robot in making a clear decision about the object type and its location. After …

We present an approach to indoor scene reconstruction from RGB-D video. The key idea is
to combine geometric registration of scene fragments with robust global optimization based …

We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar
odometry estimation. Unlike most existing lidar odometry (LO) estimations that go through …

With the advent of new, low-cost 3D sensing hardware such as the Kinect, and continued
efforts in advanced point cloud processing, 3D perception gains more and more importance …

3D object recognition in cluttered scenes is a rapidly growing research area. Based on the
used types of features, 3D object recognition methods can broadly be divided into two …

Consumer depth cameras, such as the Microsoft Kinect, are capable of providing frames of
dense depth values at real time. One fundamental question in utilizing depth cameras is how …

In this work we address the problem of feature extraction for object recognition in the context
of cameras providing RGB and depth information (RGB-D data). We consider this problem in …

Feature learning on dense 3D data has proven to be a highly successful alternative to
manual hand crafting of features. The data produced by outdoor 3D scanning devices is …

This paper introduces a taxonomy of vision systems for ground mobile robots. In the last five
years, a significant number of relevant papers have contributed to this subject. Firstly, a …

A LiDAR-based high-throughput phenoty** (HTP) system was developed for cotton plant
phenoty** in the field. The HTP system consists of a 2D LiDAR and an RTK-GPS mounted …