Vehicle self-localization is one of the most important capabilities for automated driving. Current localization methods already provide accuracy in the centimeter range, so robustness becomes a key factor, especially in urban environments. There is no commonly used standard metric for the robustness of localization systems, but a set of different approaches. Here, we show a novel robustness score that combines different aspects of robustness and evaluate a graph-based localization method with the help of fault injections. In addition, we investigate the influence of semantic class information on robustness with a layered landmark model. By using the perturbation injections and our novel robustness score for test drives, system vulnerabilities or possible improvements are identified. Furthermore, we demonstrate that semantic class information allows early discarding of misclassified dynamic objects such as pedestrians, thus improving false-positive rates. This work provides a method for the robustness evaluation of landmark-based localization systems that are also capable of measuring the impact of semantic class information for vehicle self-localization.
Highly automated driving functions currently often rely on a-priori knowledge from maps for planning and prediction in complex scenarios like cities. This makes map-relative localization an essential skill.In this paper, we address the problem of localization with automotive-grade radars, using a real-time graph-based SLAM approach. The system uses landmarks and odometry information as an abstraction layer. This way, besides radars, all kind of different sensor modalities including cameras and lidars can contribute. A single, semantic landmark map is used and maintained for all sensors.We implemented our approach using C++ and thoroughly tested it on data obtained with our test vehicles, comprising cars and trucks. Test scenarios include inner cities and industrial areas like container terminals. The experiments presented in this paper suggest that the approach is able to provide a precise and stable pose in structured environments, using radar data alone. The fusion of additional sensor information from cameras or lidars further boost performance, providing reliable semantic information needed for automated mapping.
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