Today, rail vehicle localization is based on infrastructure-side Balises (beacons) together with on-board odometry to determine whether a rail segment is occupied. Such a coarse locking leads to a sub-optimal usage of the rail networks. New railway standards propose the use of moving blocks centered around the rail vehicles to increase the capacity of the network. However, this approach requires accurate and robust position and velocity estimation of all vehicles. In this work, we investigate the applicability, challenges and limitations of current visual and visual-inertial motion estimation frameworks for rail applications. An evaluation against RTK-GPS ground truth is performed on multiple datasets recorded in industrial, sub-urban, and forest environments. Our results show that stereo visual-inertial odometry has a great potential to provide a precise motion estimation because of its complementing sensor modalities and shows superior performance in challenging situations compared to other frameworks.
@cite_10: Driver assistance systems are incorporating more and more advanced safety functions. As these functions have to react quickly and reliably in emergency situations with a false alarm rate close to zero a high integrity of the environmental perception is required. This elevated level of signal integrity can be achieved by data fusion, where the information of several, in general heterogeneous sensors is combined to obtain a better model of the environment in terms of accuracy, object integrity, object identity, etc. As an example, we demonstrate the power of sensor fusion by an automatic emergency brake (AEB) system whose environmental perception is based upon a video camera and a radar sensor. In particular we discuss the improvement of kinematic attributes such as object lateral distance as well as the object’s confidence or probability of existence.
@cite_3: A collision avoidance system for railroad vehicles needs to determine their location in the railroad network precisely and reliably. For a vehicle-based system, that is independent from the infrastructure, it is vital to determine the direction a railroad vehicle turns at switches. In this paper a vision based approach is presented that allows to achieve this reliably, even under difficult conditions. In the images of a camera that observes the area in front of a railroad vehicle the rail tracks are detected in real-time. From the perspective of the moving railroad vehicle rail tracks branch and join from to the currently travelled rail track. By tracking these rail tracks in the images, switches are detected as they are passed. It is shown that the followed track can be determined at branching switches. The approach is tested with real data from test rides in different locations and under a variety of weather conditions and environments. It proved to be very robust and of high practical use for track-selective self-localization of railroad vehicles, mandatory for collision avoidance.
@cite_2: Nowadays railway vehicle speed sensors suffer from insufficient measurement accuracy. E. g. the Doppler radar is prone to adverse weather conditions while wheel speed sensors are not sufficiently robust against wheel slip and wheel wear. However, since velocity sensors are safety relevant components, it becomes clear that conventional sensors are not able to cover all the requirements for an everyday use.
The mentioned visual-aided odometry and localization algorithms are highly specific for the rail application and do not provide the desired level of accuracy. Also, imposing such constraints to the problem might reduce the level of redundancy. not directly suitable for continuous motion estimation as they only provide information near switches @cite_3 or require manual data association @cite_2 . Furthermore, by not considering specific constraints, the visual odometry can later be fused with this information to get even more reliable and accurate pose estimation, to detect when a method is failing or to detect changes in the expected environment @cite_10 . The goal of this paper is to study the performance of generic visual-aided ego-motion estimation for the rail application.