  Considerable progress has been made in semantic scene understanding of road
scenes with monocular cameras. It is, however, mainly related to certain
classes such as cars and pedestrians. This work investigates traffic cones, an
object class crucial for traffic control in the context of autonomous vehicles.
3D object detection using images from a monocular camera is intrinsically an
ill-posed problem. In this work, we leverage the unique structure of traffic
cones and propose a pipelined approach to the problem. Specifically, we first
detect cones in images by a tailored 2D object detector; then, the spatial
arrangement of keypoints on a traffic cone are detected by our deep structural
regression network, where the fact that the cross-ratio is projection invariant
is leveraged for network regularization; finally, the 3D position of cones is
recovered by the classical Perspective n-Point algorithm. Extensive experiments
show that our approach can accurately detect traffic cones and estimate their
position in the 3D world in real time. The proposed method is also deployed on
a real-time, critical system. It runs efficiently on the low-power Jetson TX2,
providing accurate 3D position estimates, allowing a race-car to map and drive
autonomously on an unseen track indicated by traffic cones. With the help of
robust and accurate perception, our race-car won both Formula Student
Competitions held in Italy and Germany in 2018, cruising at a top-speed of 54
kmph. Visualization of the complete pipeline, mapping and navigation can be
found on our project page.
