Radar-based Automotive Localization using Landmarks in a Multimodal Sensor Graph-based Approach
This addresses localization for automated vehicles in complex urban and industrial settings, though it appears incremental as it builds on existing SLAM and multimodal fusion techniques.
The paper tackles the problem of map-relative localization for automated driving by using a real-time graph-based SLAM approach with automotive-grade radars, achieving precise and stable pose estimation in structured environments, with performance further boosted by fusing camera or lidar data.
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.