Andre-Marcel Hellmund

Bio: Andre-Marcel Hellmund is an academic researcher from Center for Information Technology. The author has contributed to research in topics: GNSS applications & Stereo camera. The author has an hindex of 4, co-authored 5 publications receiving 84 citations.

Vehicle localization with tightly coupled GNSS and visual odometry

Abstract: Accurate localization is a key task in map based autonomous driving. While in many cases high precision differential GPS is used, more and more vision based methods gain popularity to improve positioning in GNSS denied environments and to avoid high costs in high quality GNSS receivers. However, to generate a globally referenced map, satellite based methods are still important, even in vision based mapping algorithms. In this paper we present a method for integrating locally accurate visual odometry obtained from an onboard stereo camera system with satellite observations of a low cost GNSS receiver. To account for a low number of visible satellites we directly incorporate pseudorange measurements for sensor data fusion. Hence, we present a low cost satellite and camera based positioning system and evaluate it for the usage as part of an inner city mapping system.

Robot operating system: A modular software framework for automated driving

Abstract: Automated vehicles are complex systems with a high degree of interdependencies between its components. This complexity sets increasing demands for the underlying software framework. This paper firstly analyzes the requirements for software frameworks. Afterwards an overview on existing software frameworks, that have been used for automated driving projects, is provided with an in-depth introduction into an emerging open-source software framework, the Robot Operating System (ROS). After discussing the main features, advantages and disadvantages of ROS, the communication overhead of ROS is analyzed quantitatively in various configurations showing its applicability for systems with a high data load.

Multi-drive feature association for automated map generation using low-cost sensor data

Abstract: In this paper, we present an approach targeting the automated road map generation for autonomously driving vehicles using low-cost GPS sensor data in a multi-drive setup. Multiple drives with deployed commodity smartphone and stereo camera system are recorded as input data. To overcome the high position uncertainties of the GPS sensor, the GPS trajectory is fused with ego-motion estimates of the vehicle computed by visual odometry. Landmarks are extracted from the recorded imagery data and fused over all recorded drives. The resulting road map consists of a simple, parametric representation of globally referenced lane markings with low storage impact. The challenging aspect in this work is the feature association between multiple drives. Different characteristics of dashed center lines are exploited for this purpose to handle the low precision of the sensor data. The resulting association information is the building block for graph-based SLAM to optimize vehicle poses and landmarks simultaneously. The approach is finally evaluated on real world data comparing the low-precision sensor data with high-precision sensor data as ground-truth.

Application of line clustering algorithms for improving road feature detection

Abstract: Although many algorithms have been proposed for the camera-based detection of road features (such as road markings, curbstones and road borders), truly contextual or relational information between the detections is rarely used. This is all the more surprising, since a lot of potential remains unused, regarding outlier rejection or compensating detection failures, multiple detections, misclassification or fragmentation. The aim of this paper is to present an approach that is suitable for such a task in both online and offline applications as a post-processing step after the actual detection and classification step. This is achieved by adapting a perception-based line-clustering algorithm that groups the pre-classified road features based on their relations and assigns them a final class. The grouped features are then fused to form continuous lines instead of individual dashes or fragmented lines. The evaluation on a 10km drive in both rural and urban environment, as well as an online test on a short highway driving sequence shows that this approach is very well capable to increase the performance of road feature detection at a low computational cost.

Multi-drive road map generation on standardized high-velocity roads using low-cost sensor data

Abstract: Highly accurate digital road maps are the status quo for advanced driver assistance systems and automated driving functions. To achieve high map accuracies, recent approaches deployed high-cost and high-precision sensor systems. This paper proposes a novel automated map generation framework for standardized high-velocity roads using a low-cost sensor system composed of a monocular camera and a low-precision GNSS receiver. To compensate for the lower precision, the map is generated from multiple measurement drives.

Global Positioning System Theory And Practice

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Vehicle localization with tightly coupled GNSS and visual odometry

Abstract: Accurate localization is a key task in map based autonomous driving. While in many cases high precision differential GPS is used, more and more vision based methods gain popularity to improve positioning in GNSS denied environments and to avoid high costs in high quality GNSS receivers. However, to generate a globally referenced map, satellite based methods are still important, even in vision based mapping algorithms. In this paper we present a method for integrating locally accurate visual odometry obtained from an onboard stereo camera system with satellite observations of a low cost GNSS receiver. To account for a low number of visible satellites we directly incorporate pseudorange measurements for sensor data fusion. Hence, we present a low cost satellite and camera based positioning system and evaluate it for the usage as part of an inner city mapping system.

Making Bertha Cooperate–Team AnnieWAY’s Entry to the 2016 Grand Cooperative Driving Challenge

Abstract: This paper presents the concepts and methods utilized by Team AnnieWAY for the 2016 Grand Cooperative Driving Challenge. The paper introduces the automated vehicle BerthaOne . The vehicle, even though being based on the Bertha platform, distinguishes itself from its siblings by its software modules and algorithms. We, therefore, describe its system architecture and algorithms for perception, cooperation and motion planning. In Particular, we present a motion planner that plans different maneuvers flexibly by augmenting the cost function with situation specific cost terms. We subsequently describe the requirements of the 2016 GCDC and evaluate our performance during the competition.

Accurate and Efficient Self-Localization on Roads using Basic Geometric Primitives

Abstract: Highly accurate localization with very limited amount of memory and computational power is one of the big challenges for next generation series cars. We propose localization based on geometric primitives which are compact in representation and further valuable for other tasks like planning and behavior generation. The primitives lack distinctive signature which makes association between detections and map elements highly ambiguous. We resolve ambiguities early in the pipeline by online building up a local map which is key to runtime efficiency. Further, we introduce a new framework to fuse association and odometry measurements based on robust pose graph optimization.We evaluate our localization framework on over 30 min of data recorded in urban scenarios. Our map is memory efficient with less than 8 kB/km and we achieve high localization accuracy with a mean position error of less than 10 cm and a mean yaw angle error of less than 0. 25° at a localization update rate of 50Hz.

Drift with Devil: Security of Multi-Sensor Fusion based Localization in High-Level Autonomous Driving under GPS Spoofing (Extended Version).

Abstract: For high-level Autonomous Vehicles (AV), localization is highly security and safety critical. One direct threat to it is GPS spoofing, but fortunately, AV systems today predominantly use Multi-Sensor Fusion (MSF) algorithms that are generally believed to have the potential to practically defeat GPS spoofing. However, no prior work has studied whether today's MSF algorithms are indeed sufficiently secure under GPS spoofing, especially in AV settings. In this work, we perform the first study to fill this critical gap. As the first study, we focus on a production-grade MSF with both design and implementation level representativeness, and identify two AV-specific attack goals, off-road and wrong-way attacks. To systematically understand the security property, we first analyze the upper-bound attack effectiveness, and discover a take-over effect that can fundamentally defeat the MSF design principle. We perform a cause analysis and find that such vulnerability only appears dynamically and non-deterministically. Leveraging this insight, we design FusionRipper, a novel and general attack that opportunistically captures and exploits take-over vulnerabilities. We evaluate it on 6 real-world sensor traces, and find that FusionRipper can achieve at least 97% and 91.3% success rates in all traces for off-road and wrong-way attacks respectively. We also find that it is highly robust to practical factors such as spoofing inaccuracies. To improve the practicality, we further design an offline method that can effectively identify attack parameters with over 80% average success rates for both attack goals, with the cost of at most half a day. We also discuss promising defense directions.