Sparse octree algorithms for scalable dense volumetric tracking and mapping

This thesis is concerned with the problem of Simultaneous Localisation and Mapping (SLAM), the task of localising an agent within an unknown environment and at the same time building a representation of it. In particular, we tackle the fundamental scalability limitations of dense volumetric SLAM systems. We do so by proposing a highly efficient hierarchical data-structure based on octrees together with a set of algorithms to support the most compute-intensive operations in typical volumetric reconstruction pipelines. We employ our hierarchical representation in a novel dense pipeline based on occupancy probabilities. Crucially, the complete space representation encoded by the octree enables to demonstrate a fully integrated system in which tracking, mapping and occupancy queries can be performed seamlessly on a single coherent representation. While achieving accuracy either at par or better than the current state-of-the-art, we demonstrate run-time performance of at least an order of magnitude better than currently available hierarchical data-structures. Finally, we introduce a novel multi-scale reconstruction system that exploits our octree hierarchy. By adaptively selecting the appropriate scale to match the effective sensor resolution in both integration and rendering, we demonstrate better reconstruction results and tracking accuracy compared to single-resolution grids. Furthermore, we achieve much higher computational performance by propagating information up and down the tree in a lazy fashion, which allow us to reduce the computational load when updating distant surfaces. We have released our software as an open-source library, named supereight, which is freely available for the benefit of the wider community. One of the main advantages of our library is its flexibility. By carefully providing a set of algorithmic abstractions, supereight enables SLAM practitioners to freely experiment with different map representations with no intervention on the back-end library code and crucially, preserving performance. Our work has been adopted by robotics researchers in both academia and industry.Open Acces

INSPEX: Make environment perception available as a portable system

International audienceObstacle avoidance systems for autonomous vehicles combine multiple sensing technologies (i.e. LiDAR, Radar, Ultrasound and Visual) to detect different types of obstacles across the full range of lighting and weather conditions. Sensor data are fused with vehicle orientation (obtained for instance from an Inertial Measurement Unit and/or compass) and navigation subsystems. Power hungry, they require powerful computational capability, which limits their use to high-end vehicles and robots. 2 INSPEX ambition The H2020 INSPEX project plans to make obstacle detection capabilities available as a personal portable multi-sensors, miniaturised, low power device. This device will detect, locate and warn of obstacles under different environmental conditions, in indoor/outdoor environments, with static and mobile obstacles. Potential applications range from safer human navigation in reduced visibility conditions (e.g. for first responders and fire brigades), small robot/drone obstacle avoidance systems to navigation for the visually and mobility impaired people. As primary demonstrator (Fig.1), we will plug the INSPEX device on a white cane (see Fig. 1) for Visually Impaired and Blind (VIB) people to detect obstacle over the whole person height, provide audio feedback about harmful obstacles, improve their mobility confidence and reduce injuries, especially at waist and head levels [1]. The device will offer a "safety cocoon" to its user

INSPEX: design and integration of a portable/wearable smart spatial exploration system

The INSPEX H2020 project main objective is to integrate automotive-equivalent spatial exploration and obstacle detection functionalities into a portable/wearable multi-sensor, miniaturised, low power device. The INSPEX system will detect and localise in real-time static and mobile obstacles under various environmental conditions in 3D. Potential applications range from safer human navigation in reduced visibility, small robot/drone obstacle avoidance systems to navigation for the visually/mobility impaired, this latter being the primary use-case considered in the project

EFFICIENT INFORMATION INTEGRATION SYSTEM FOR TEMPORAL AND SPATIAL DATA

In this dissertation, I develop a novel inconsistency detection and data fusion method for data integration systems. Inconsistent data may lead to incorrect query results and induce unexplainable outcomes. I propose an inconsistency detection method to find out which data items (e.g., temporal or spatial report) have the higher potential to cause data conflicts as well as to estimate a reasonable consistent reported value. My approach is based on representing overlapping data reports as a characteristic linear system. The characteristic linear system can be used to estimate consistent reported values within overlapping time and space intervals. I explore applicability of the proposed approach in different domains. In particular, I perform temporal data fusion with time-overlapping reports using a historical database. I also experiment with spatial data fusion involving space-overlapping reports using simulation of sensor data sets of robots performing search and rescue task. Finally, I apply the proposed approach to combine temporal and spatial fusion and demonstrate that such multidimensional fusion improves inconsistency detection and target value estimation