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EVALUATING INTEGRITY FOR MOBILE ROBOT LOCALIZATION SAFETY
Title
EVALUATING INTEGRITY FOR MOBILE ROBOT LOCALIZATION SAFETY
Creator
Duenas Arana, Guillermo
Date
2019
Description
Precise localization is paramount for autonomous navigation. Localization errors are not only dangerous by themselves, but can also mislead...
Show morePrecise localization is paramount for autonomous navigation. Localization errors are not only dangerous by themselves, but can also mislead other dependent systems into moving to a hazardous location. Unfortunately, the problem of quantifying robot localization safety is only sparsely addressed in the robotics literature, and most robotics algorithms still quantify pose estimation performance using a covariance matrix or particle spread, which only accounts for nominal sensor errors. This is insufficient for life- and mission-critical applications, such as autonomous vehicles and other co-robots, where ignoring sensor or sensor or processing faults can lead to catastrophic localization errors. Thus, other methods must be employed to ensure safety.In response, this research leverages prior work in aviation integrity monitoring to tackle the more challenging case of evaluating localization safety in mobile robots. In contrast to aviation applications, that heavily rely on the Global Navigation Satellite System (GNSS) for localization, robots often operate in complex, GNSS-denied environments that require a more sophisticated sensor suite to ensure localization safety. Localization integrity risk is the probability that a robot's pose estimate lies outside pre-defined acceptable limits while no alarm is triggered. In this work, the integrity risk is rigorously upper bounded by accounting for both nominal noise and other non-nominal sensor faults, resulting in a safe upper bound on the localization integrity risk.The main contribution of this dissertation is the design and evaluation of a sequential integrity monitoring methodology applicable to mobile robot localization algorithms that use feature extraction and data association. First, faults introduced during the feature extraction and data association processes are distinguished, and the probability of the latter is rigorously upper bounded using analytical methods. The impact of faults in the estimate error's and fault detector's distributions is then determined to quantify integrity risk, which is evaluated under the worst-possible fault combination. To determine the impact of previous faults without a boundlessly growing number of fault hypotheses, this dissertation presents a novel method that uses a preceding time window to build a limited set of hypotheses and a prior estimate bias to account for faults occurring before the start of the time window. The proposed methodology is applicable to Kalman Filter and fixed-lag smoothing localization. Simulated and experimental results are presented to validate the methodology.
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Improving Localization Safety for Landmark-Based LiDAR Localization System
Title
Improving Localization Safety for Landmark-Based LiDAR Localization System
Creator
Chen, Yihe
Date
2024
Description
Autonomous ground robots have gained traction in various commercial applications, with established safety protocols covering subsystem...
Show moreAutonomous ground robots have gained traction in various commercial applications, with established safety protocols covering subsystem reliability, control algorithm stability, path planning, and localization. This thesis specifically delves into the localizer, a critical component responsible for determining the vehicle’s state (e.g., position and orientation), assessing compliance with localization safety requirements, and proposing methods for enhancing localization safety.Within the robotics domain, diverse localizers are utilized, such as scan-matching techniques like normal distribution transformations (NDT), the iterative closest point (ICP) algorithm,probabilistic maps method, and semantic map-based localization.Notably, NDT stands out as a widely adopted standalone laser localization method, prevalent in autonomous driving software such as Autoware and Apollo platforms.In addition to the mentioned localizers, common state estimators include variants of Kalman Filter, particle filter-based, and factor graph-based estimators. The evaluation of localization performance typically involves quantifying the estimated state variance for these state estimators.While various localizer options exist, this study focuses on those utilizing extended Kalman filters and factor graph methods. Unlike methods like NDT and ICP algorithms, extended Kalman filters and factor graph based approaches guarantee bounding of estimated state uncertainty and have been extensively researched for integrity monitoring.Common variance analysis, employed for sensor readings and state estimators, has limitations, primarily focusing on non-faulted scenarios under nominal conditions. This approach proves impractical for real-world scenarios and falls short for safety-critical applications like autonomous vehicles (AVs).To overcome these limitations, this thesis utilizes a dedicated safety metric: integrity risk. Integrity risk assesses the reliability of a robot’s sensory readings and localization algorithm performance under both faulted and non-faulted conditions. With a proven track record in aviation, integrity risk has recently been applied to robotics applications, particularly for evaluating the safety of lidar localization.Despite the significance of improving localization integrity risk through laser landmark manipulation, this remains an under explored territory. Existing research on robot integrity risk primarily focuses on the vehicles themselves. To comprehensively understand the integrity risk of a lidar-based localization system, as addressed in this thesis, an exploration of lidar measurement faults’ modes is essential, a topic covered in this thesis.The primary contributions of this thesis include: A realistic error estimation method for state estimators in autonomous vehicles navigating using pole-shape lidar landmark maps, along with a compensatory method; A method for quantifying the risk associated with unmapped associations in urban environments, enhancing the realism of values provided by the integrity risk estimator; a novel approach to improve the localization integrity of autonomous vehicles equipped with lidar feature extractors in urban environments through minimal environmental modifications, mitigating the impact of unmapped association faults. Simulation results and experimental results are presented and discussed to illustrate the impact of each method, providing further insights into their contributions to localization safety.
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