Jan Rohde

Precise vehicle localization in dense urban environments

In this contribution we introduce a framework for precise vehicle localization in dense urban environments which are characterized by high rates of dynamic and semi-static objects. The proposed localization method is specifically designed to handle inconsistencies between map material and sensor measurements. This is achieved by means of a robust map matching procedure based on the Fourier-Mellin transformation (FMT) for global vehicle pose estimation. Accurate and reliable relative localization is obtained from a LiDAR odometry. Consistency checks based on the cumulative sum (CUSUM) test are instrumented for rejection of inconsistent map matching results from the fusion procedure. Our key contributions are: i) Introduction and adaptation of a spectral map matching procedure based on the FMT for urban automated driving, ii) Presentation of a framework for efficient and robust localization in dense urban environments based on a novel LiDAR odometry, map matching, wheel odometry and GPS, iii) Proposal of a procedure for localization integrity monitoring which leads to significantly increased pose estimation accuracy. Evaluation results show the superior performance of the proposed approach compared to another state-of-the-art localization algorithm for a challenging urban dataset. All maps were recorded two years in advance of the evaluation test run. Furthermore, different LiDAR-based sensor setups were used for mapping and localization.

Localization accuracy estimation with application to perception design

Landmark-based localization in dynamic environments poses high demands on the perception system of a mobile robot. The pose estimate generally has to fulfill specific accuracy requirements which might be necessitated by dependent systems, such as behavior planning. Thus, in this contribution we focus on the model-based derivation of perception requirements, i.e. detectable landmark types and minimum detection rates, to enable global localization with a specified upper bound on uncertainty. To this end, we utilize stochastic geometry to accurately capture and explicitly consider characteristics of the dynamic environment (e.g. occlusions), and the perception system (e.g. missed detections). From this point our contributions are twofold: i) We propose an analytical model of upper bounds on localization uncertainty. For continuous pose tracking, the Kalman filter equations for intermittent observations are considered and ii) perception requirements, i.e. minimum detection rates, based on specified upper bounds on pose estimation uncertainty are derived. Monte Carlo simulations are used to demonstrate the performance of the proposed methods.

Model-Based Derivation of Perception Accuracy Requirements for Vehicle Localization in Urban Environments

In this contribution, we address the model-based derivation of perception requirements based on upper bounds on vehicle localization uncertainty for urban driver assistance (UDA) and urban automated driving (UAD). We show that a probabilistic model for the estimation of map-relative localization accuracy can be obtained and utilized for proper parametrization of a perception system. Therefore, the paper at hand entails two main contributions: i) Proposal of a probabilistic model for localization accuracy in closed form under the assumption of a generic measurement model with Gaussian noise and a stochastic landmark distribution, ii) Presentation of a framework for model-based derivation of perception requirements which permit desired localization performance. To exemplify the application of our method, sensor parameters for a stereo vision system (e.g. stereo base-width) are determined and verified via comprehensive simulation experiments. This is conducted in the context of an urban automated lane keeping system under explicit consideration of non-existent or occluded lane markings and curb stones.

Perzeption für robuste Fahrzeuglokalisierung

Eine hinreichend genaue und zuverlassige Fahrzeuglokalisierung ist ein fundamentaler Bestandteil vieler moderner Fahrerassistenz‐ und hochautomatisierter Fahrzeugsysteme. Die Schatzung einer globalen Fahrzeugpose ist haufig erforderlich um zusatzliche Informationen, wie z.B. Fahrbahnverlaufe, aus einer digitalen Karte zu entnehmen und als Grundlage fur eine fundierte Verhaltensplanung und ‐entscheidung verwenden zu konnen. Minimalanforderungen an die Genauigkeit der Fahrzeuglokalisierung resultieren aus Art und Anwendungskontext der verwendeten Umfeldinformationen.