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_8: In structure-from-motion with a single camera it is well known that the scene can be only recovered up to a scale. In order to compute the absolute scale, one needs to know the baseline of the camera motion or the dimension of at least one element in the scene. In this paper, we show that there exists a class of structure-from-motion problems where it is possible to compute the absolute scale completely automatically without using this knowledge, that is, when the camera is mounted on wheeled vehicles (e.g. cars, bikes, or mobile robots). The construction of these vehicles puts interesting constraints on the camera motion, which are known as “nonholonomic constraints”. The interesting case is when the camera has an offset to the vehicle's center of motion. We show that by just knowing this offset, the absolute scale can be computed with a good accuracy when the vehicle turns. We give a mathematical derivation and provide experimental results on both simulated and real data over a large image dataset collected during a 3 Km path. To our knowledge this is the first time nonholonomic constraints of wheeled vehicles are used to estimate the absolute scale. We believe that the proposed method can be useful in those research areas involving visual odometry and mapping with vehicle mounted cameras.
@cite_6: We present a self-supervised approach to ignoring "distractors" in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each input image, which we use to train a deep convolutional network. At run-time we use the predicted ephemerality and depth as an input to a monocular visual odometry (VO) pipeline, using either sparse features or dense photometric matching. Our approach yields metric-scale VO using only a single camera and can recover the correct egomotion even when 90 of the image is obscured by dynamic, independently moving objects. We evaluate our robust VO methods on more than 400km of driving from the Oxford RobotCar Dataset and demonstrate reduced odometry drift and significantly improved egomotion estimation in the presence of large moving vehicles in urban traffic.
@cite_5: For robotic systems to realise lifelong autonomy they must be able to navigate accurately in changing environments. In this thesis we describe, implement and validate a new approach to the problem of long-term navigation. To begin, we present our stereo visual odometry system which provides highly accurate pose estimation. Our approach combines several techniques found in existing implementations and a recently published image descriptor that simplifies the solution architecture. The performance and versatility of our system is demonstrated through testing on multiple datasets. Equipped with our visual odometry system, we describe a new approach to the problem of lifelong navigation. We learn a model whose complexity varies naturally in accordance with the variation of scene appearance. As the robot repeatedly traverses its workspace, it accumulates distinct visual experiences that, in concert, implicitly represent the scene variation - each experience captures a visual mode. When operating in a previously visited area, we continually try to localise in these previous experiences while simultaneously running the visual odometry. Failure to localise in a sufficient number of prior experiences indicates an insufficient model of the workspace and instigates the laying down of the live image sequence as a new distinct experience. In this way, over time we can capture the typical temporally varying appearance of an environment and the number of experiences required tends to a constant. Although we focus on vision as a primary sensor, the ideas we present here are equally applicable to other sensor modalities. We demonstrate our approach working on a road vehicle operating over a three month period at different times of day, in different weather and lighting conditions.
@cite_13: 
@cite_11: Odometry is a safety on-board subsystem of modern railway Automatic Train Protection (ATP) and Automatic Train Control (ATC) and his main task is the estimation of instantaneous speed and the travelled distance of the railway vehicle. An accurate estimation is mandatory, because an error (residual) on the train position may lead to a dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, the proposed algorithm exploits data fusion of different inputs coming from a redundant sensor layout: in particular, the proposed strategy consists of a sensor fusion between the information coming from a tachometer and an IMU (Inertial Measurements Unit) is carried out. A 3D multibody model has been designed so at to simulate the sensor outputs. Within the framework of the presented research, a custom IMU, designed by ECM S.p.a. has been built. The IMU board is then tested via a dedicated HIL test rig (Hardware in the Loop) that includes an industrial robot able to reproduce the motion of the railway vehicle. The performances of the innovative localization algorithm have been evaluated by generating the experimental outputs. The main aim of this work is the development of an innovative localization algorithm for railway vehicles able to enhance the speed and position estimation accuracy of the classical odometry algorithms, such as the Italian SCMT (Sistema Controllo Marcia Treno). The results highlight a good improvement of the position and speed estimation performances compared to those obtained with classical SCMT algorithms, currently in use on the Italian railway network
In automotive applications, many of the challenges such as high velocities and constraint motion are similar to the rail domain. There, odometry is often solved by using wheel encoders @cite_11 @cite_13 as they do not suffer from high slip as in rail applications. Furthermore, stereo vision @cite_5 and monocular vo with learned depth @cite_6 have also been used successfully for ego motion estimation. A multitude of state-of-the - art stereo-visual odometry frameworks are tested for automotive applications in the visual odometry part of the KITTI Vision Benchmark Suite . One popular and well performing open-source pipeline is . Unfortunately, the KITTI dataset does not include synchronized imu measurements and therefore does not allow in-depth insights into the benefits inertial data could provide. Finally, the scale of the motion can also be retrieved by exploiting non-holonomic constraints @cite_8 which, however, relies on frequent turns.