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Integrity based landmark generation: A method to generate landmark configurations that guarantee mobile robot localization safety
Title
Integrity based landmark generation: A method to generate landmark configurations that guarantee mobile robot localization safety
Creator
Chen, Yihe
Date
2020
Description
From the bronze-age city Nineveh to the modern metropolitan like Tokyo, traffic shape cities and profoundly affect the life of people. Similar...
Show moreFrom the bronze-age city Nineveh to the modern metropolitan like Tokyo, traffic shape cities and profoundly affect the life of people. Similar to how the wide-spreading of automobile had modified the modern cities in early 20th century, we are now standing on the eve of yet another traffic revolution. With the vast spreading of autonomous/semi- autonomous robotics application, it is important for the urban designers to design or retrofit urban environment that is safe and friendly to the autonomous robots; As more robots are deployed in life-critical situations, such as autonomous passenger vehicles, it is imperative to consider their safety, and in particular, their localization safety. While it would be ideal to guarantee safety in any environment without having to physically modify said environment, this is not always possible and one may have add landmarks or active beacons to reach an acceptable level of safety for landmark-based localization. Localization safety is assessed using integrity, the primary safety metric used in open-sky aviation applications that has been recently applied to mobile robots and can ac- count for the impact of rarely occurring, undetected faults. Conventional integrity monitor- ing method has high dependency on GPS system, while the traditional Global Navigation Satellite System - Inertia Measurement Unit (GNSS-IMU) based localization does not ap- plied in the metropolitan areas due to the signal blocking and multi-pathing problem caused by high-rise structures. Thus, this dissertation concentrates on the feature based integrity monitoring method. This dissertation formulates environmental localization safety problem as a system- atic optimization problem: given the robot’s trajectory and the current landmark map, add the minimal number of new landmarks at certain location such that the integrity risk along the trajectory is below a given safety threshold. This dissertation proposes two algorithms to solve the problem: Integrity-based Landmark Generator (I-LaG) and Fast I-LaG. I-LaG adds fewer landmarks but it is relatively computationally expensive; Fast I-LaG is less com- putationally intensive at the expense of more landmarks. Both simulation and experimental results are presented.
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Quantifying Localization Safety for State-of-the-Art Mobile Robot Estimation Algorithms
Title
Quantifying Localization Safety for State-of-the-Art Mobile Robot Estimation Algorithms
Creator
Abdul Hafez, Osama Mutie Fahad
Date
2023
Description
In mobile robotics, localization safety is quantified using covariance matrix or particle spread.However, such methods are insufficient for...
Show moreIn mobile robotics, localization safety is quantified using covariance matrix or particle spread.However, such methods are insufficient for mission or life-critical applications, like Autonomous Vehicles (AVs), because they only reflect nominal sensor noise without considering sensor measurement faults. Sensor faults are unknown deterministic errors that cannot be modeled using a zero mean Gaussian distribution. Ignoring sensor faults, in such applications, might result in large localization errors, which in turn deceives other reliant systems, like the controller, leading to catastrophic consequences, such as traffic accidents for AVs. Thus, other techniques need to be used to conservatively quantify pose safety.This thesis builds upon previous research in aviation safety, or what is referred to as \textit{integrity monitoring}, to quantify localization safety for mobile robots that use state-of-the-art state estimators (as localizers).Specifically, this thesis utilizes the localization \textit{integrity risk} metric, as a measure of localization safety, which is defined as the probability of the robot's pose estimate error to lie outside pre-determined acceptable limits while an alarm is not triggered. Unlike open-sky aviation applications, where Global Navigation Satellite Systems (GNSS) signals are available, mobile robots operate in GNSS-denied, or in the best case GNSS-degraded, environments, which demands utilizing more complex set of sensors to guarantee an acceptable level of localization safety. This thesis provides a conservative measure of localization safety by rigorously upper-bounding the integrity risk while accounting for both nominal lidar noise and unmodeled lidar measurement faults.The contributions of this thesis include the design and analysis of practical integrity monitoring and failure detection procedures for mobile robots utilizing map-based particle filtering, a recursive integrity monitoring method for mobile robots utilizing map-based fixed lag smoothing for both solution-separation and chi-squared as failure detectors, the synthesis of an integrity monitoring procedure for mobile robots utilizing Extended Kalman Filter-based Simultaneous Localization And Mapping (EKF-based SLAM), and a Model Predictive Control (MPC) framework that is capable of planning mobile robot's trajectory to follow a predefined robot path while maintaining a predefined minimum level of mobile robot localization safety. The proposed methodologies are validated using both simulation and experimental results conducted in real-world urban university campus environments.
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