Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization

Many robotics applications require precise pose estimates despite operating in large and changing environments. This can be addressed by visual localization, using a pre-computed 3D model of the surroundings. The pose estimation then amounts to finding correspondences between 2D keypoints in a query image and 3D points in the model using local descriptors. However, computational power is often limited on robotic platforms, making this task challenging in large-scale environments. Binary feature descriptors significantly speed up this 2D-3D matching, and have become popular in the robotics community, but also strongly impair the robustness to perceptual aliasing and changes in viewpoint, illumination and scene structure. In this work, we propose to leverage recent advances in deep learning to perform an efficient hierarchical localization. We first localize at the map level using learned image-wide global descriptors, and subsequently estimate a precise pose from 2D-3D matches computed in the candidate places only. This restricts the local search and thus allows to efficiently exploit powerful non-binary descriptors usually dismissed on resource-constrained devices. Our approach results in state-of-the-art localization performance while running in real-time on a popular mobile platform, enabling new prospects for robotics research.Comment: CoRL 2018 Camera-ready (fix typos and update citations

Visual communication in urban planning and urban design

This report documents the current status of visual communication in urban design and planning. Visual communication is examined through discussion of standalone and network media, specifically concentrating on visualisation on the World Wide Web(WWW).Firstly, we examine the use of Solid and Geometric Modelling for visualising urban planning and urban design. This report documents and compares examples of the use of Virtual Reality Modelling Language (VRML) and proprietary WWW based Virtual Reality modelling software. Examples include the modelling of Bath and Glasgow using both VRML 1.0 and 2.0. A review is carried out on the use of Virtual Worldsand their role in visualising urban form within multi-user environments. The use of Virtual Worlds is developed into a case study of the possibilities and limitations of Virtual Internet Design Arenas (ViDAs), an initiative undertaken at the Centre for Advanced Spatial Analysis, University College London. The use of Virtual Worlds and their development towards ViDAs is seen as one of the most important developments in visual communication for urban planning and urban design since the development plan.Secondly, photorealistic media in the process of communicating plans is examined.The process of creating photorealistic media is documented, examples of the Virtual Streetscape and Wired Whitehall Virtual Urban Interface System are provided. The conclusion is drawn that although the use of photo-realistic media on the WWW provides a way to visually communicate planning information, its use is limited. The merging of photorealistic media and solid geometric modelling is reviewed in the creation of Augmented Reality. Augmented Reality is seen to provide an important step forward in the ability to quickly and easily visualise urban planning and urban design information.Thirdly, the role of visual communication of planning data through GIS is examined interms of desktop, three dimensional and Internet based GIS systems. The evolution to Internet GIS is seen as a critical component in the development of virtual cities which will allow urban planners and urban designers to visualise and model the complexity of the built environment in networked virtual reality.Finally a viewpoint is put forward of the Virtual City, linking Internet GIS with photorealistic multi-user Virtual Worlds. At present there are constraints on how far virtual cities can be developed, but a view is provided on how these networked virtual worlds are developing to aid visual communication in urban planning and urban design

Biophilic urban developments following dynamic flows of tree-shaped architectures

Latest theories and practices in Biophilic designs of the urban space regard the urban fabric as being composed of several interrelated layers of energetic structure influencing each other in a non-linear manner primarily. The interaction between two or more interfaces of the urban space layers evolves into new and non-predictable properties. Evolution and creation of new boundaries/interfaces follows laws related to fractal growth; most of the times this particular evolution is defined by laws of physics, such as Thermodynamics and Constructal Law. Designs that do not follow these laws may produce anti-natural and hostile environments, which do not fit into human beings’ evolution, and thus, fail to enhance life by all means. The author of this paper should like to illustrate how new developments of urbanism worldwide currently work upon conceptual and town planning models based not only upon cutting-edge technology, but also upon natural laws and patterns of life and human behaviours strictly related to flaws and movement dictated by natural phenomena. When abrupt interruption of the urban structure has occurred, a consequent design solution does not even guarantee flowing and freedom to morph. It is impossible to create harmonic designs which naturally “unite the animate with the inanimate”, as Adrian Bejan and Sylvie Lorente affirm, whenever urban sprawl fails to encompass Biophilic solutions related to tree-shaped architectures. The author argues that Constructal invasion into the urban space “as fundamental problems of access to flow: volume to point, area to point, line to point, and the respective reverse flow directions” can only guarantee high standard quality of life in either contemporary or future cities developments

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability

Radar-on-Lidar: metric radar localization on prior lidar maps

Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar maps for final 2D positioning. We demonstrate the effectiveness of the proposed localization framework on the public multi-session dataset. The experimental results show that our system can achieve high accuracy for long-term localization in outdoor scenes

LocNet: Global localization in 3D point clouds for mobile vehicles

Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.Comment: 6 pages, IV 2018 accepte