Abstract-This paper proposes a new visual SLAM technique that not only integrates 6 degrees of freedom (DOF) pose and dense structure but also simultaneously integrates the colour information contained in the images over time. This involves developing an inverse model for creating a super-resolution map from many low resolution images. Contrary to classic superresolution techniques, this is achieved here by taking into account full 3D translation and rotation within a dense localisation and mapping framework. This not only allows to take into account the full range of image deformations but also allows to propose a novel criteria for combining the low resolution images together based on the difference in resolution between different images in 6D space. Another originality of the proposed approach with respect to the current state of the art lies in the minimisation of both colour (RGB) and depth (D) errors, whilst competing approaches only minimise geometry. Several results are given showing that this technique runs in real-time (30Hz) and is able to map large scale environments in high-resolution whilst simultaneously improving the accuracy and robustness of the tracking.
Extending these ideas to RGB-D sensors, Meilland and Comport REF propose a visual SLAM technique that simultaneously super-resolves intensity image and depth-map based on a Kinect sensor.
Super-resolution 3D tracking and mapping
2013 IEEE International Conference on Robotics and Automation
2013 IEEE International Conference on Robotics and Automation
Engineering Computer Science