Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation

The monocular vision-based simultaneous localization and mapping (vSLAM) is one of the most challenging problem in mobile robotics and computer vision. In this work we study the post-processing techniques applied to sparse 3D point-cloud maps, obtained by feature-based vSLAM algorithms. Map post-processing is split into 2 major steps: 1) noise and outlier removal and 2) upsampling. We evaluate different combinations of known algorithms for outlier removing and upsampling on datasets of real indoor and outdoor environments and identify the most promising combination. We further use it to convert a point-cloud map, obtained by the real UAV performing indoor flight to 3D voxel grid (octo-map) potentially suitable for path planning.Comment: 10 pages, 4 figures, camera-ready version of paper for "The 3rd International Conference on Interactive Collaborative Robotics (ICR 2018)

Object Registration in Semi-cluttered and Partial-occluded Scenes for Augmented Reality

This paper proposes a stable and accurate object registration pipeline for markerless augmented reality applications. We present two novel algorithms for object recognition and matching to improve the registration accuracy from model to scene transformation via point cloud fusion. Whilst the first algorithm effectively deals with simple scenes with few object occlusions, the second algorithm handles cluttered scenes with partial occlusions for robust real-time object recognition and matching. The computational framework includes a locally supported Gaussian weight function to enable repeatable detection of 3D descriptors. We apply a bilateral filtering and outlier removal to preserve edges of point cloud and remove some interference points in order to increase matching accuracy. Extensive experiments have been carried to compare the proposed algorithms with four most used methods. Results show improved performance of the algorithms in terms of computational speed, camera tracking and object matching errors in semi-cluttered and partial-occluded scenes

Rough terrain motion planning for actuated, tracked robots

Traversing challenging structures like boulders, rubble, stairs and steps, mobile robots need a special level of mobility. Robots with reconfigurable chassis are able to alter their configuration to overcome such structures. This paper presents a two-stage motion planning scheme for reconfigurable robots in rough terrain. First, we consider the robots operating limits rather than the complete states to quickly find an initial path in a low dimensional space. Second, we identify path segments which lead through rough areas of the environment and refine those segments using the entire robot state including the actuator configurations. We present a roadmap and a RRT* method to perform the path refinement. Our algorithm does not rely on any detailed structure/terrain categorization or on any predefined motion sequences. Hence, our planner can be applied to urban structures, like stairs, as well as rough unstructured environments