Map-Based Localization for Unmanned Aerial Vehicle Navigation

Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments. Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments. The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Self location of vision guided autonomous mobile robots.

Lau Ah Wai, Calvin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 108-111).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- An Overview --- p.4Chapter 1.1.1 --- Robot Self Location --- p.4Chapter 1.1.2 --- Robot Navigation --- p.10Chapter 1.2 --- Scope of Thesis --- p.12Chapter 2 --- Theory --- p.13Chapter 2.1 --- Coordinate Systems Transformations --- p.13Chapter 2.2 --- Problem Specification --- p.21Chapter 2.3 --- The Process of Stereo Vision --- p.22Chapter 2.3.1 --- Disparity and Depth --- p.22Chapter 2.3.2 --- Vertical Edge Detection and Extraction --- p.25Chapter 2.3.3 --- Line Matching Using Dynamic Programming --- p.27Chapter 3 --- Mobile Robot Self Location --- p.29Chapter 3.1 --- Physical Points by Stereo Reconstruction --- p.29Chapter 3.1.1 --- Physical Points Refinement --- p.32Chapter 3.2 --- Motion Uncertainties Modeling --- p.33Chapter 3.3 --- Improved Physical Point Estimations by EKF --- p.36Chapter 3.4 --- Matching Physical Points to Model by Geometric Hashing --- p.40Chapter 3.4.1 --- Similarity Invariant --- p.44Chapter 3.5 --- Initial Pose Estimation --- p.47Chapter 3.5.1 --- Initial Pose Refinement --- p.50Chapter 3.6 --- Self Location Using Other Camera Combinations --- p.50Chapter 4 --- Improvements to Self Location Using Bayesian Inference --- p.55Chapter 4.1 --- Statistical Characteristics of Edges --- p.57Chapter 4.2 --- Evidence at One Pixel --- p.60Chapter 4.3 --- Evidence Over All Pixels --- p.62Chapter 4.4 --- A Simplification Of Geometric Hashing --- p.62Chapter 4.4.1 --- Simplification of The Similarity Invariant --- p.63Chapter 4.4.2 --- Translation Invariant --- p.63Chapter 4.4.3 --- Simplification to The Hashing Table --- p.65Chapter 5 --- Robot Navigation --- p.67Chapter 5.1 --- Propagation of Motion Uncertainties to Estimated Pose --- p.68Chapter 5.2 --- Expectation Map Derived from the CAD Model --- p.70Chapter 6 --- Experimental Results --- p.74Chapter 6.1 --- Results Using Simulated Environment --- p.74Chapter 6.1.1 --- Results and Error Analysis --- p.75Chapter 6.2 --- Results Using Real Environment --- p.85Chapter 6.2.1 --- Camera Calibration Using Tsai's Algorithm --- p.85Chapter 6.2.2 --- Pose Estimation By Geometric Hashing --- p.88Chapter 6.2.3 --- Pose Estimation by Bayesian Inference and Geometric Hash- ing --- p.90Chapter 6.2.4 --- Comparison of Self Location Approaches --- p.92Chapter 6.2.5 --- Motion Tracking --- p.93Chapter 7 --- Conclusion and Discussion --- p.95Chapter 7.1 --- Conclusion and Discussion --- p.95Chapter 7.2 --- Contributions --- p.97Chapter 7.3 --- Subjects for Future Research --- p.98Chapter A --- Appendix --- p.100Chapter A.1 --- Extended Kalman Filter --- p.100Chapter A.2 --- Visualizing Uncertainty for 2D Points --- p.10

Building an enhanced vocabulary of the robot environment with a ceiling pointing camera

Mobile robots are of great help for automatic monitoring tasks in different environments. One of the first tasks that needs to be addressed when creating these kinds of robotic systems is modeling the robot environment. This work proposes a pipeline to build an enhanced visual model of a robot environment indoors. Vision based recognition approaches frequently use quantized feature spaces, commonly known as Bag of Words (BoW) or vocabulary representations. A drawback using standard BoW approaches is that semantic information is not considered as a criteria to create the visual words. To solve this challenging task, this paper studies how to leverage the standard vocabulary construction process to obtain a more meaningful visual vocabulary of the robot work environment using image sequences. We take advantage of spatio-temporal constraints and prior knowledge about the position of the camera. The key contribution of our work is the definition of a new pipeline to create a model of the environment. This pipeline incorporates (1) tracking information to the process of vocabulary construction and (2) geometric cues to the appearance descriptors. Motivated by long term robotic applications, such as the aforementioned monitoring tasks, we focus on a configuration where the robot camera points to the ceiling, which captures more stable regions of the environment. The experimental validation shows how our vocabulary models the environment in more detail than standard vocabulary approaches, without loss of recognition performance. We show different robotic tasks that could benefit of the use of our visual vocabulary approach, such as place recognition or object discovery. For this validation, we use our publicly available data-set

InLoc: Indoor Visual Localization with Dense Matching and View Synthesis

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor environments. The method proceeds along three steps: (i) efficient retrieval of candidate poses that ensures scalability to large-scale environments, (ii) pose estimation using dense matching rather than local features to deal with textureless indoor scenes, and (iii) pose verification by virtual view synthesis to cope with significant changes in viewpoint, scene layout, and occluders. Second, we collect a new dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. Third, we demonstrate that our method significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data

The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection

Where am I? This is one of the most critical questions that any intelligent system should answer to decide whether it navigates to a previously visited area. This problem has long been acknowledged for its challenging nature in simultaneous localization and mapping (SLAM), wherein the robot needs to correctly associate the incoming sensory data to the database allowing consistent map generation. The significant advances in computer vision achieved over the last 20 years, the increased computational power, and the growing demand for long-term exploration contributed to efficiently performing such a complex task with inexpensive perception sensors. In this article, visual loop closure detection, which formulates a solution based solely on appearance input data, is surveyed. We start by briefly introducing place recognition and SLAM concepts in robotics. Then, we describe a loop closure detection system's structure, covering an extensive collection of topics, including the feature extraction, the environment representation, the decision-making step, and the evaluation process. We conclude by discussing open and new research challenges, particularly concerning the robustness in dynamic environments, the computational complexity, and scalability in long-term operations. The article aims to serve as a tutorial and a position paper for newcomers to visual loop closure detection.Comment: 25 pages, 15 figure