Comparative Design Space Exploration of Dense and Semi-Dense SLAM

SLAM has matured significantly over the past few years, and is beginning to appear in serious commercial products. While new SLAM systems are being proposed at every conference, evaluation is often restricted to qualitative visualizations or accuracy estimation against a ground truth. This is due to the lack of benchmarking methodologies which can holistically and quantitatively evaluate these systems. Further investigation at the level of individual kernels and parameter spaces of SLAM pipelines is non-existent, which is absolutely essential for systems research and integration. We extend the recently introduced SLAMBench framework to allow comparing two state-of-the-art SLAM pipelines, namely KinectFusion and LSD-SLAM, along the metrics of accuracy, energy consumption, and processing frame rate on two different hardware platforms, namely a desktop and an embedded device. We also analyze the pipelines at the level of individual kernels and explore their algorithmic and hardware design spaces for the first time, yielding valuable insights.Comment: IEEE International Conference on Robotics and Automation 201

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

Information metrics for localization and mapping

Decades of research have made possible the existence of several autonomous systems that successfully and efficiently navigate within a variety of environments under certain conditions. One core technology that has allowed this is simultaneous localization and mapping (SLAM), the process of building a representation of the environment while localizing the robot in it. State-of-the-art solutions to the SLAM problem still rely, however, on heuristic decisions and options set by the user. In this thesis we search for principled solutions to various aspects of the localization and mapping problem with the help of information metrics. One such aspect is the issue of scalability. In SLAM, the problem size grows indefinitely as the experiment goes by, increasing computational resource demands. To maintain the problem tractable, we develop methods to build an approximation to the original network of constraints of the SLAM problem by reducing its size while maintaining its sparsity. In this thesis we propose three methods to build the topology of such approximated network, and two methods to perform the approximation itself. In addition, SLAM is a passive application. It means, it does not drive the robot. The problem of driving the robot with the aim of both accurately localizing the robot and mapping the environment is called active SLAM. In this problem two normally opposite forces drive the robot, one to new places discovering unknown regions and another to revisit previous configurations to improve localization. As opposed to heuristics, in this thesis we pose the problem as the joint minimization of both map and trajectory estimation uncertainties, and present four different active SLAM approaches based on entropy-reduction formulation. All methods presented in this thesis have been rigorously validated in both synthetic and real datasets.Dècades de recerca han fet possible l’existència de nombrosos sistemes autònoms que naveguen eficaçment i eficient per varietat d’entorns sota certes condicions. Una de les principals tecnologies que ho han fet possible és la localització i mapeig simultanis (SLAM), el procés de crear una representació de l’entorn mentre es localitza el robot en aquesta. De tota manera, els algoritmes d’SLAM de l’estat de l’art encara basen moltes decisions en heurístiques i opcions a escollir per l’usuari final. Aquesta tesi persegueix solucions fonamentades per a varietat d’aspectes del problema de localització i mappeig amb l’ajuda de mesures d’informació. Un d’aquests aspectes és l’escalabilitat. En SLAM, el problema creix indefinidament a mesura que l’experiment avança fent créixer la demanda de recursos computacionals. Per mantenir el problema tractable, desenvolupem mètodes per construir una aproximació de la xarxa de restriccions original del problema d’SLAM, reduint així el seu tamany a l’hora que es manté la seva naturalesa dispersa. En aquesta tesi, proposem tres métodes per confeccionar la topologia de l’approximació i dos mètodes per calcular l’aproximació pròpiament. A més, l’SLAM és una aplicació passiva. És a dir que no dirigeix el robot. El problema de guiar el robot amb els objectius de localitzar el robot i mapejar l’entorn amb precisió es diu SLAM actiu. En aquest problema, dues forces normalment oposades guien el robot, una cap a llocs nous descobrint regions desconegudes i l’altra a revisitar prèvies configuracions per millorar la localització. En contraposició amb mètodes heurístics, en aquesta tesi plantegem el problema com una minimització de l’incertesa tant en el mapa com en l’estimació de la trajectòria feta i presentem quatre mètodes d’SLAM actiu basats en la reducció de l’entropia. Tots els mètodes presentats en aquesta tesi han estat rigurosament validats tant en sèries de dades sintètiques com en reals