This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student Germany (FSG) 2019 driverless competition, where it won 1st place overall. The presented solution combines early fusion of camera and LiDAR data, a layered mapping approach, and a planning approach that uses Bayesian filtering to achieve high-speed driving on unknown race tracks while creating accurate maps. We benchmark the method against our team's previous solution, which won FSG 2018, and show improved accuracy when driving at the same speeds. Furthermore, the new pipeline makes it possible to reliably raise the maximum driving speed in unknown environments from 3 m/s to 12 m/s while still mapping with an acceptable RMSE of 0.29 m.

Dense vegetation is an example of a structurally complex environment that robots encounter in many outdoor applications such as agriculture, environmental monitoring, or search and rescue. For a robot to safely navigate or perform manipulation tasks in dense vegetation, it is important to detect and distinguish rigid branches from the surrounding soft foliage. This task is challenging for traditional sensing approaches, such as vision, because the foliage can partially or totally occlude the branches. We present a haptic sensing system capable of detecting contact with the vegetation and locating branches occluded by soft foliage. The system follows a vision-based tactile sensing approach consisting of an array of compliant whiskers, with fiducial markers attached to them, and a camera to track their displacement. When the whiskers are inserted in the vegetation and the array is oscillated, rigid branches cause the whiskers to deflect significantly, while deflections caused by softer foliage are smaller. We developed a sampling strategy and a software pipeline to detect the location of the branches based on the deflection magnitude of the whiskers. Upon indoor and outdoor experiments with artificial and natural vegetation, we demonstrate the ability to locate branches among compliant foliage.