3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection

Cameras are a crucial exteroceptive sensor for self-driving cars as they are low-cost and small, provide appearance information about the environment, and work in various weather conditions. They can be used for multiple purposes such as visual navigation and obstacle detection. We can use a surround multi-camera system to cover the full 360-degree field-of-view around the car. In this way, we avoid blind spots which can otherwise lead to accidents. To minimize the number of cameras needed for surround perception, we utilize fisheye cameras. Consequently, standard vision pipelines for 3D mapping, visual localization, obstacle detection, etc. need to be adapted to take full advantage of the availability of multiple cameras rather than treat each camera individually. In addition, processing of fisheye images has to be supported. In this paper, we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project. This project seeks to enable automated valet parking for self-driving cars. Our pipeline is able to precisely calibrate multi-camera systems, build sparse 3D maps for visual navigation, visually localize the car with respect to these maps, generate accurate dense maps, as well as detect obstacles based on real-time depth map extraction

Recognising, Representing and Mapping Natural Features in Unstructured Environments

This thesis addresses the problem of building statistical models for multi-sensor perception in unstructured outdoor environments. The perception problem is divided into three distinct tasks: recognition, representation and association. Recognition is cast as a statistical classification problem where inputs are images or a combination of images and ranging information. Given the complexity and variability of natural environments, this thesis investigates the use of Bayesian statistics and supervised dimensionality reduction to incorporate prior information and fuse sensory data. A compact probabilistic representation of natural objects is essential for many problems in field robotics. This thesis presents techniques for combining non-linear dimensionality reduction with parametric learning through Expectation Maximisation to build general representations of natural features. Once created these models need to be rapidly processed to account for incoming information. To this end, techniques for efficient probabilistic inference are proposed. The robustness of localisation and mapping algorithms is directly related to reliable data association. Conventional algorithms employ only geometric information which can become inconsistent for large trajectories. A new data association algorithm incorporating visual and geometric information is proposed to improve the reliability of this task. The method uses a compact probabilistic representation of objects to fuse visual and geometric information for the association decision. The main contributions of this thesis are: 1) a stochastic representation of objects through non-linear dimensionality reduction; 2) a landmark recognition system using a visual and ranging sensors; 3) a data association algorithm combining appearance and position properties; 4) a real-time algorithm for detection and segmentation of natural objects from few training images and 5) a real-time place recognition system combining dimensionality reduction and Bayesian learning. The theoretical contributions of this thesis are demonstrated with a series of experiments in unstructured environments. In particular, the combination of recognition, representation and association algorithms is applied to the Simultaneous Localisation and Mapping problem (SLAM) to close large loops in outdoor trajectories, proving the benefits of the proposed methodology

Robotic Cameraman for Augmented Reality based Broadcast and Demonstration

In recent years, a number of large enterprises have gradually begun to use vari-ous Augmented Reality technologies to prominently improve the audiences’ view oftheir products. Among them, the creation of an immersive virtual interactive scenethrough the projection has received extensive attention, and this technique refers toprojection SAR, which is short for projection spatial augmented reality. However,as the existing projection-SAR systems have immobility and limited working range,they have a huge difficulty to be accepted and used in human daily life. Therefore,this thesis research has proposed a technically feasible optimization scheme so thatit can be practically applied to AR broadcasting and demonstrations. Based on three main techniques required by state-of-art projection SAR applica-tions, this thesis has created a novel mobile projection SAR cameraman for ARbroadcasting and demonstration. Firstly, by combining the CNN scene parsingmodel and multiple contour extractors, the proposed contour extraction pipelinecan always detect the optimal contour information in non-HD or blurred images.This algorithm reduces the dependency on high quality visual sensors and solves theproblems of low contour extraction accuracy in motion blurred images. Secondly, aplane-based visual mapping algorithm is introduced to solve the difficulties of visualmapping in these low-texture scenarios. Finally, a complete process of designing theprojection SAR cameraman robot is introduced. This part has solved three mainproblems in mobile projection-SAR applications: (i) a new method for marking con-tour on projection model is proposed to replace the model rendering process. Bycombining contour features and geometric features, users can identify objects oncolourless model easily. (ii) a camera initial pose estimation method is developedbased on visual tracking algorithms, which can register the start pose of robot to thewhole scene in Unity3D. (iii) a novel data transmission approach is introduced to establishes a link between external robot and the robot in Unity3D simulation work-space. This makes the robotic cameraman can simulate its trajectory in Unity3D simulation work-space and project correct virtual content. Our proposed mobile projection SAR system has made outstanding contributionsto the academic value and practicality of the existing projection SAR technique. Itfirstly solves the problem of limited working range. When the system is running ina large indoor scene, it can follow the user and project dynamic interactive virtualcontent automatically instead of increasing the number of visual sensors. Then,it creates a more immersive experience for audience since it supports the user hasmore body gestures and richer virtual-real interactive plays. Lastly, a mobile systemdoes not require up-front frameworks and cheaper and has provided the public aninnovative choice for indoor broadcasting and exhibitions

Bridging text spotting and SLAM with junction features

Navigating in a previously unknown environment and recognizing naturally occurring text in a scene are two important autonomous capabilities that are typically treated as distinct. However, these two tasks are potentially complementary, (i) scene and pose priors can benefit text spotting, and (ii) the ability to identify and associate text features can benefit navigation accuracy through loop closures. Previous approaches to autonomous text spotting typically require significant training data and are too slow for real-time implementation. In this work, we propose a novel high-level feature descriptor, the “junction”, which is particularly well-suited to text representation and is also fast to compute. We show that we are able to improve SLAM through text spotting on datasets collected with a Google Tango, illustrating how location priors enable improved loop closure with text features.Andrea Bocelli FoundationEast Japan Railway CompanyUnited States. Office of Naval Research (N00014-10-1-0936, N00014-11-1-0688, N00014-13-1-0588)National Science Foundation (U.S.) (IIS-1318392

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