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)

Spectral analysis for long-term robotic mapping

This paper presents a new approach to mobile robot mapping in long-term scenarios. So far, the environment models used in mobile robotics have been tailored to capture static scenes and dealt with the environment changes by means of ‘memory decay’. While these models keep up with slowly changing environments, their utilization in dynamic, real world environments is difficult. The representation proposed in this paper models the environment’s spatio-temporal dynamics by its frequency spectrum. The spectral representation of the time domain allows to identify, analyse and remember regularly occurring environment processes in a computationally efficient way. Knowledge of the periodicity of the different environment processes constitutes the model predictive capabilities, which are especially useful for long-term mobile robotics scenarios. In the experiments presented, the proposed approach is applied to data collected by a mobile robot patrolling an indoor environment over a period of one week. Three scenarios are investigated, including intruder detection and 4D mapping. The results indicate that the proposed method allows to represent arbitrary timescales with constant (and low) memory requirements, achieving compression rates up to 106 . Moreover, the representation allows for prediction of future environment’s state with ∼ 90% precision