Mapping, Localization and Path Planning for Image-based Navigation using Visual Features and Map

Building on progress in feature representations for image retrieval, image-based localization has seen a surge of research interest. Image-based localization has the advantage of being inexpensive and efficient, often avoiding the use of 3D metric maps altogether. That said, the need to maintain a large number of reference images as an effective support of localization in a scene, nonetheless calls for them to be organized in a map structure of some kind. The problem of localization often arises as part of a navigation process. We are, therefore, interested in summarizing the reference images as a set of landmarks, which meet the requirements for image-based navigation. A contribution of this paper is to formulate such a set of requirements for the two sub-tasks involved: map construction and self-localization. These requirements are then exploited for compact map representation and accurate self-localization, using the framework of a network flow problem. During this process, we formulate the map construction and self-localization problems as convex quadratic and second-order cone programs, respectively. We evaluate our methods on publicly available indoor and outdoor datasets, where they outperform existing methods significantly.Comment: CVPR 2019, for implementation see https://github.com/janinethom

Transit Performance Measures in California

This research is the result of a California Department of Transportation (Caltrans) request to assess the most commonly available transit performance measures in California. Caltrans wanted to understand performance measures and data used by Metropolitan Planning Organizations (MPOs) and transit agencies to help it develop statewide measures. This report serves as a summary reference guide to help Caltrans understand the numerous and diverse performance measures used by MPOs and transit agencies in California. First, investigators review the available literature to identify a complete transit performance framework for the purposes of organizing agency measures, metrics, and data sources. Next, they review the latest transit performance measures documented in planning reports for the four largest MPOs in California (San Francisco Bay Area, Los Angeles, San Diego, and Sacramento). Researchers pay special attention to the transit performance measures used by these MPOs, because these measures are available for the majority of California’s population. Finally, investigators summarize 231 performance measures used by a total 26 local transit agencies in the State of California, based on transit planning documents available on the internet

Spectral comparison of large urban graphs

The spectrum of an axial graph is proposed as a means for comparison between spaces, particularly for measuring between very large and complex graphs. A number of methods have been used in recent years for comparative analysis within large sets of urban areas, both to investigate properties of specific known types of street network or to propose a taxonomy of urban morphology based on an analytical technique. In many cases, a single or small range of predefined, scalar measures such as metric distance, integration, control or clustering coefficient have been used to compare the graphs. While these measures are well understood theoretically, their low dimensionality determines the range of observations that can ultimately be drawn from the data. Spectral analysis consists of a high dimensional vector representing each space, between which metric distance may be measured to indicate the overall difference between two spaces, or subspaces may be extracted to correspond to certain features. It is used for comparison of entire urban graphs, to determine similarities (and differences) in their overall structure. Results are shown of a comparison of 152 cities distributed around the world. The clustering of cities of similar properties in a high dimensional space is discussed. Principal and nonlinear components of the data set indicate significant correlations in the graph similarities between cities and their proximity to one another, suggesting that cultural features based on location are evident in the city form and that these can be quantified by the proposed method. Results of classification tests show that a city’s location can be estimated based purely on its form. The high dimensionality of the spectra is beneficial for its utility in data-mining applications that can draw correlations with other data sets such as land use information. It is shown how further processing by supervised learning allows the extraction of relevant features. A methodological comparison is also drawn with statistical studies that use a strong correlation between human genetic markers and geographical location of populations to derive detailed reconstructions of prehistoric migration. Thus, it is suggested that the method may be utilised for mapping the transfer of cultural memes by measuring comparison between cities

Helping humans and agents avoid undesirable consequences with models of intervention

2021 Fall.Includes bibliographical references.When working in an unfamiliar online environment, it can be helpful to have an observer that can intervene and guide a user toward a desirable outcome while avoiding undesirable outcomes or frustration. The Intervention Problem is deciding when to intervene in order to help a user. The Intervention Problem is similar to, but distinct from, Plan Recognition because the observer must not only recognize the intended goals of a user but also when to intervene to help the user when necessary. In this dissertation, we formalize a family of intervention problems to address two sub-problems: (1) The Intervention Recognition Problem, and (2) The Intervention Recovery Problem. The Intervention Recognition Problem views the environment as a state transition system where an agent (or a human user), in order to achieve a desirable outcome, executes actions that change the environment from one state to the next. Some states in the environment are undesirable and the user does not have the ability to recognize them and the intervening agent wants to help the user in the environment avoid the undesirable state. In this dissertation, we model the environment as a classical planning problem and discuss three intervention models to address the Intervention Recognition Problem. The three models address different dimensions of the Intervention Recognition Problem, specifically the actors in the environment, information hidden from the intervening agent, type of observations and noise in the observations. The first model: Intervention by Recognizing Actions Enabling Multiple Undesirable Consequences, is motivated by a study where we observed how home computer users practice cyber-security and take action to unwittingly put their online safety at risk. The model is defined for an environment where three agents: the user, the attacker and the intervening agent are present. The intervening agent helps the user reach a desirable goal that is hidden from the intervening agent by recognizing critical actions that enable multiple undesirable consequences. We view the problem of recognizing critical actions as a multi-factor decision problem of three domain-independent metrics: certainty, timeliness and desirability. The three metrics simulate the trade-off between the safety and freedom of the observed agent when selecting critical actions to intervene. The second model: Intervention as Classical Planning, we model scenarios where the intervening agent observes a user and a competitor attempting to achieve different goals in the same environment. A key difference in this model compared to the first model is that the intervening agent is aware of the user's desirable goal and the undesirable state. The intervening agent exploits the classical planning representation of the environment and uses automated planning to project the possible outcomes in the environment exactly and approximately. To recognize when intervention is required, the observer analyzes the plan suffixes leading to the user's desirable goal and the undesirable state and learns the differences between the plans that achieve the desirable goal and plans that achieve the undesirable state using machine learning. Similar to the first model, learning the differences between the safe and unsafe plans allows the intervening agent to balance specific actions with those that are necessary for the user to allow some freedom. The third model: Human-aware Intervention, we assume that the user is a human solving a cognitively engaging planning task. When human users plan, unlike an automated planner, they do not have the ability to use heuristics to search for the best solution. They often make mistakes and spend time exploring the search space of the planning problem. The complication this adds to the Intervention Recognition Problem is that deciding to intervene by analyzing plan suffixes generated by an automated planner is no longer feasible. Using a cognitively engaging puzzle solving task (Rush Hour) we study how human users solve the puzzle as a planning task and develop the Human-aware Intervention model combining automated planning and machine learning. The intervening agent uses a domain specific feature set more appropriate for human behavior to decide in real time whether to intervene the human user. Our experiments using the benchmark planning domains and human subject studies show that the three intervention recognition models out performs existing plan recognition algorithms in predicting when intervention is required. Our solution to address the Intervention Recovery Problem goes beyond the typical preventative measures to help the human user recover from intervention. We propose the Interactive Human-aware Intervention where a human user solves a cognitively engaging planning task with the assistance of an agent that implements the Human-aware Intervention. The Interactive Human-aware Intervention is different from typical preventive measures where the agent executes actions to modify the domain such that the undesirable plan can not progress (e.g., block an action). Our approach interactively guides the human user toward the solution to the planning task by revealing information about the remaining planning task. We evaluate the Interactive Human-aware Intervention using both subjective and objective measures in a human subject study