A robust RGB-D SLAM algorithm

125 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “Six research publications about evaluating ego-motion estimation and SLAM over TUM Benchmark dataset are [21, 38, 45, 84, 86, 87].”
Real-time large-scale dense RGB-D SLAM with volumetric fusion (2014, methodology): “Their system allows deformation of open triangular meshes and point clouds; no connectivity information is required as is the case with many deformation algorithms (Karan (2000); Jacobson and Sorkine (2011)).”
3-D Mapping With an RGB-D Camera (2014, background): “[23] recently proposed a SLAM system that switches between bundle adjustment with and without available depth, which makes it more robust to lack of depth information,”
An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics (2015, background): “Hu et al. [57] outlined the problem of not having enough depth information in large rooms due to the limitations of RGB-D cameras.”
The role of RGB-D benchmark datasets: an overview (2013, background): “A tabular overview about the corresponding publications for each dataset can found in Table 1.”