Localization in urban environments. A hybrid interval-probabilistic method

Ensuring safety has become a paramount concern with the increasing autonomy of vehicles and the advent of autonomous driving. One of the most fundamental tasks of increased autonomy is localization, which is essential for safe operation. To quantify safety requirements, the concept of integrity has been introduced in aviation, based on the ability of the system to provide timely and correct alerts when the safe operation of the systems can no longer be guaranteed. Therefore, it is necessary to assess the localization's uncertainty to determine the system's operability. In the literature, probability and set-membership theory are two predominant approaches that provide mathematical tools to assess uncertainty. Probabilistic approaches often provide accurate point-valued results but tend to underestimate the uncertainty. Set-membership approaches reliably estimate the uncertainty but can be overly pessimistic, producing inappropriately large uncertainties and no point-valued results. While underestimating the uncertainty can lead to misleading information and dangerous system failure without warnings, overly pessimistic uncertainty estimates render the system inoperative for practical purposes as warnings are fired more often. This doctoral thesis aims to study the symbiotic relationship between set-membership-based and probabilistic localization approaches and combine them into a unified hybrid localization approach. This approach enables safe operation while not being overly pessimistic regarding the uncertainty estimation. In the scope of this work, a novel Hybrid Probabilistic- and Set-Membership-based Coarse and Refined (HyPaSCoRe) Localization method is introduced. This method localizes a robot in a building map in real-time and considers two types of hybridizations. On the one hand, set-membership approaches are used to robustify and control probabilistic approaches. On the other hand, probabilistic approaches are used to reduce the pessimism of set-membership approaches by augmenting them with further probabilistic constraints. The method consists of three modules - visual odometry, coarse localization, and refined localization. The HyPaSCoRe Localization uses a stereo camera system, a LiDAR sensor, and GNSS data, focusing on localization in urban canyons where GNSS data can be inaccurate. The visual odometry module computes the relative motion of the vehicle. In contrast, the coarse localization module uses set-membership approaches to narrow down the feasible set of poses and provides the set of most likely poses inside the feasible set using a probabilistic approach. The refined localization module further refines the coarse localization result by reducing the pessimism of the uncertainty estimate by incorporating probabilistic constraints into the set-membership approach. The experimental evaluation of the HyPaSCoRe shows that it maintains the integrity of the uncertainty estimation while providing accurate, most likely point-valued solutions in real-time. Introducing this new hybrid localization approach contributes to developing safe and reliable algorithms in the context of autonomous driving

Enhancing State Estimator for Autonomous Race Car : Leveraging Multi-modal System and Managing Computing Resources

This paper introduces an innovative approach to enhance the state estimator for high-speed autonomous race cars, addressing challenges related to unreliable measurements, localization failures, and computing resource management. The proposed robust localization system utilizes a Bayesian-based probabilistic approach to evaluate multimodal measurements, ensuring the use of credible data for accurate and reliable localization, even in harsh racing conditions. To tackle potential localization failures during intense racing, we present a resilient navigation system. This system enables the race car to continue track-following by leveraging direct perception information in planning and execution, ensuring continuous performance despite localization disruptions. Efficient computing resource management is critical to avoid overload and system failure. We optimize computing resources using an efficient LiDAR-based state estimation method. Leveraging CUDA programming and GPU acceleration, we perform nearest points search and covariance computation efficiently, overcoming CPU bottlenecks. Real-world and simulation tests validate the system's performance and resilience. The proposed approach successfully recovers from failures, effectively preventing accidents and ensuring race car safety.Comment: arXiv admin note: text overlap with arXiv:2207.1223

Chance-Constrained Optimal Path Planning With Obstacles

Autonomous vehicles need to plan trajectories to a specified goal that avoid obstacles. For robust execution, we must take into account uncertainty, which arises due to uncertain localization, modeling errors, and disturbances. Prior work handled the case of set-bounded uncertainty. We present here a chance-constrained approach, which uses instead a probabilistic representation of uncertainty. The new approach plans the future probabilistic distribution of the vehicle state so that the probability of failure is below a specified threshold. Failure occurs when the vehicle collides with an obstacle or leaves an operator-specified region. The key idea behind the approach is to use bounds on the probability of collision to show that, for linear-Gaussian systems, we can approximate the nonconvex chance-constrained optimization problem as a disjunctive convex program. This can be solved to global optimality using branch-and-bound techniques. In order to improve computation time, we introduce a customized solution method that returns almost-optimal solutions along with a hard bound on the level of suboptimality. We present an empirical validation with an aircraft obstacle avoidance example.National Science Foundation (U.S.) (Grant IIS-1017992)Boeing Company (Grant MIT-BA-GTA-1

Learning Tractable Probabilistic Models for Fault Localization

In recent years, several probabilistic techniques have been applied to various debugging problems. However, most existing probabilistic debugging systems use relatively simple statistical models, and fail to generalize across multiple programs. In this work, we propose Tractable Fault Localization Models (TFLMs) that can be learned from data, and probabilistically infer the location of the bug. While most previous statistical debugging methods generalize over many executions of a single program, TFLMs are trained on a corpus of previously seen buggy programs, and learn to identify recurring patterns of bugs. Widely-used fault localization techniques such as TARANTULA evaluate the suspiciousness of each line in isolation; in contrast, a TFLM defines a joint probability distribution over buggy indicator variables for each line. Joint distributions with rich dependency structure are often computationally intractable; TFLMs avoid this by exploiting recent developments in tractable probabilistic models (specifically, Relational SPNs). Further, TFLMs can incorporate additional sources of information, including coverage-based features such as TARANTULA. We evaluate the fault localization performance of TFLMs that include TARANTULA scores as features in the probabilistic model. Our study shows that the learned TFLMs isolate bugs more effectively than previous statistical methods or using TARANTULA directly.Comment: Fifth International Workshop on Statistical Relational AI (StaR-AI 2015

Connection Between System Parameters and Localization Probability in Network of Randomly Distributed Nodes

This article deals with localization probability in a network of randomly distributed communication nodes contained in a bounded domain. A fraction of the nodes denoted as L-nodes are assumed to have localization information while the rest of the nodes denoted as NL nodes do not. The basic model assumes each node has a certain radio coverage within which it can make relative distance measurements. We model both the case radio coverage is fixed and the case radio coverage is determined by signal strength measurements in a Log-Normal Shadowing environment. We apply the probabilistic method to determine the probability of NL-node localization as a function of the coverage area to domain area ratio and the density of L-nodes. We establish analytical expressions for this probability and the transition thresholds with respect to key parameters whereby marked change in the probability behavior is observed. The theoretical results presented in the article are supported by simulations.Comment: To appear on IEEE Transactions on Wireless Communications, November 200

Generalized Kernel-based Visual Tracking

In this work we generalize the plain MS trackers and attempt to overcome standard mean shift trackers' two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a robust template model for kernel-based MS tracking. In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data. Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background. We adopt a support vector machine (SVM) for training. The tracker is then implemented by maximizing the classification score. An iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page