Distributed scene reconstruction from multiple mobile platforms

Recent research on mobile robotics has produced new designs that provide house-hold robots with omnidirectional motion. The image sensor embedded in these devices motivates the application of 3D vision techniques on them for navigation and mapping purposes. In addition to this, distributed cheapsensing systems acting as unitary entity have recently been discovered as an efficient alternative to expensive mobile equipment. In this work we present an implementation of a visual reconstruction method, structure from motion (SfM), on a low-budget, omnidirectional mobile platform, and extend this method to distributed 3D scene reconstruction with several instances of such a platform. Our approach overcomes the challenges yielded by the plaform. The unprecedented levels of noise produced by the image compression typical of the platform is processed by our feature filtering methods, which ensure suitable feature matching populations for epipolar geometry estimation by means of a strict quality-based feature selection. The robust pose estimation algorithms implemented, along with a novel feature tracking system, enable our incremental SfM approach to novelly deal with ill-conditioned inter-image configurations provoked by the omnidirectional motion. The feature tracking system developed efficiently manages the feature scarcity produced by noise and outputs quality feature tracks, which allow robust 3D mapping of a given scene even if - due to noise - their length is shorter than what it is usually assumed for performing stable 3D reconstructions. The distributed reconstruction from multiple instances of SfM is attained by applying loop-closing techniques. Our multiple reconstruction system merges individual 3D structures and resolves the global scale problem with minimal overlaps, whereas in the literature 3D mapping is obtained by overlapping stretches of sequences. The performance of this system is demonstrated in the 2-session case. The management of noise, the stability against ill-configurations and the robustness of our SfM system is validated on a number of experiments and compared with state-of-the-art approaches. Possible future research areas are also discussed

Single and multiple stereo view navigation for planetary rovers

© Cranfield UniversityThis thesis deals with the challenge of autonomous navigation of the ExoMars rover. The absence of global positioning systems (GPS) in space, added to the limitations of wheel odometry makes autonomous navigation based on these two techniques - as done in the literature - an inviable solution and necessitates the use of other approaches. That, among other reasons, motivates this work to use solely visual data to solve the robot’s Egomotion problem. The homogeneity of Mars’ terrain makes the robustness of the low level image processing technique a critical requirement. In the first part of the thesis, novel solutions are presented to tackle this specific problem. Detection of robust features against illumination changes and unique matching and association of features is a sought after capability. A solution for robustness of features against illumination variation is proposed combining Harris corner detection together with moment image representation. Whereas the first provides a technique for efficient feature detection, the moment images add the necessary brightness invariance. Moreover, a bucketing strategy is used to guarantee that features are homogeneously distributed within the images. Then, the addition of local feature descriptors guarantees the unique identification of image cues. In the second part, reliable and precise motion estimation for the Mars’s robot is studied. A number of successful approaches are thoroughly analysed. Visual Simultaneous Localisation And Mapping (VSLAM) is investigated, proposing enhancements and integrating it with the robust feature methodology. Then, linear and nonlinear optimisation techniques are explored. Alternative photogrammetry reprojection concepts are tested. Lastly, data fusion techniques are proposed to deal with the integration of multiple stereo view data. Our robust visual scheme allows good feature repeatability. Because of this, dimensionality reduction of the feature data can be used without compromising the overall performance of the proposed solutions for motion estimation. Also, the developed Egomotion techniques have been extensively validated using both simulated and real data collected at ESA-ESTEC facilities. Multiple stereo view solutions for robot motion estimation are introduced, presenting interesting benefits. The obtained results prove the innovative methods presented here to be accurate and reliable approaches capable to solve the Egomotion problem in a Mars environment

A Survey on Global LiDAR Localization

Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. In the last two decades, LiDAR scanners have become a standard sensor for robot localization and mapping. This article surveys recent progress and advances in LiDAR-based global localization. We start with the problem formulation and explore the application scope. We then present the methodology review covering various global localization topics, such as maps, descriptor extraction, and consistency checks. The contents are organized under three themes. The first is the combination of global place retrieval and local pose estimation. Then the second theme is upgrading single-shot measurement to sequential ones for sequential global localization. The third theme is extending single-robot global localization to cross-robot localization on multi-robot systems. We end this survey with a discussion of open challenges and promising directions on global lidar localization

Robust and Fast 3D Scan Alignment using Mutual Information

This paper presents a mutual information (MI) based algorithm for the estimation of full 6-degree-of-freedom (DOF) rigid body transformation between two overlapping point clouds. We first divide the scene into a 3D voxel grid and define simple to compute features for each voxel in the scan. The two scans that need to be aligned are considered as a collection of these features and the MI between these voxelized features is maximized to obtain the correct alignment of scans. We have implemented our method with various simple point cloud features (such as number of points in voxel, variance of z-height in voxel) and compared the performance of the proposed method with existing point-to-point and point-to- distribution registration methods. We show that our approach has an efficient and fast parallel implementation on GPU, and evaluate the robustness and speed of the proposed algorithm on two real-world datasets which have variety of dynamic scenes from different environments