Filter-based Pose Estimation for Electric Vehicles Relative to a Ground-based Charging Platform Using On-board Camera Images

Efficient inductive charging of electric vehicles requires the accurate alignment of the vehicle above a ground-based inductive charging platform. This can be achieved using onboard cameras observing the charging platform during the approach manoeuvre. In this contribution, a method for vehicle pose estimation relative to the charging platform is proposed. Vehicle dynamics data obtained from independent pose estimates relying on either markers or markerless features are fused by Kalman filtering to mutual vehicle pose estimates, wherefore the method can be used even when the vehicle is close to the charging platform and the markers are no longer visible in the on-board camera images. The method has been tested with simulated data along trajectories of typical approach manoeuvers to parking lots with a charging platform at the end of the trajectories. Independent from the vehicle motion model and the tested trajectory, deviations of estimated positions from the ground truth of around 0.5 m at the charging platform have been achieved when using dynamics data from both independent pose estimates with an extended Kalman filter. 1 Inductive charging of electric vehicles at charging platforms Development of electro mobility leads to new requirements for vehicles and external infrastructure. Among these requirements, supplying power to the vehicles plays an important role. Nowadays, plug-in electric vehicles carry a battery providing energy to one or more electrical engines. Typically, batteries of such vehicles are charged using a wired connection between the vehicle and a power socket at a charging station. Alternatives to wired connections are exchanging the battery at battery swapping stations (either stationary or mobile) (SHAO et al. 2017) or doing inductive charging (HWANG et al. 2016). With inductive charging, townscapes don’t get changed by many charging stations along the streets and charging becomes more comfortable for the drivers of electric vehicles, to mention just two advantages. Power can be supplied via inductive charging both if the vehicles are being driven or being parked. In both cases, vehicles and roads or parking lots have to be equipped with electromagnetic coils for the power transfer to the vehicle. According to PANCHAL et al. (2018), two aspects have strong negative influence on the efficiency of the inductive charging process of electric vehicles: A large air-gap (ground clearance of vehicles) and misalignment between the coils at the vehicle and in the roads or parking lots. Methods for reducing 1 Technical University of Munich, Photogrammetry & Remote Sensing, Arcisstraße 21, D-80333 Munich, E-Mail: [alexander.hanel, stilla]@tum.de 2 ServiceXpert Gesellschaft für Service-Informationssysteme mbH, Wandsbeker Allee 77, D-22041 Hamburg, E-Mail: [prajwala.sudi, stephan.pfenninger]@servicexpert.de 3 Technical University of Munich, Chair of Media Technology, Arcisstraße 21, D-80333 Munich, E-Mail: eckehard.steinbach@tum.de Dreiländertagung der DGPF, der OVG und der SGPF in Wien, Österreich – Publikationen der DGPF, Band 28, 2019 55 the air-gap to increase the efficiency are often limited, for example to vehicles with adaptive air suspension. In contrast, the degree of misalignment can be reduced by placing the vehicle accurately above the coils. Accurate placement can be realized especially for inductive charging platforms installed in parking lots, where specific approach manoeuvres can be driven. When approaching the platform with the vehicle, the induced voltage can be used up to a maximal misalignment of around 60 cm (induced voltage has almost reached zero) as a measure for fine alignment (HWANG et al. 2016). Coarse alignment enabling a smooth approach manoeuvre requires therefore a different method. One possibility would be to use the vehicle position obtained by GPS to determine the relative pose to the charging platform and to guide the vehicle closer to the charging platform. This approach is not always practical as for example in dense urban areas with limited space vehicles are often parked in parking garages, where typically no GPS signal is available. For such situations, a possibility would be to detect the charging platform in images acquired by an environment-observing optical on-board camera in the vehicle during the approach manoeuvre and to derive the relative pose between vehicle and charging platform. Compared to other typical environment-observing sensors in vehicles like LiDAR or radar, optical cameras are relatively cheap (e.g. BMW parts catalogue, e.g. available at BMWFANS.INFO 2017) and mounted at several places of a car providing a good overview over the environment around the car on different sides (e.g. ZIEBINSKI et al. 2016). Among all cameras, front-looking ones are considered as most important for this task, as they observe the upcoming driveway. For both autonomously-driven cars relying on cameras for environment perception and human drivers, modern charging platforms installed completely below the road surface (e.g. Bombardier primove, BOMBARDIER TRANSPORTATION 2013), could be made visible for example by special markers attached to the ground. Common markers for photogrammetric applications, e.g. for vehicle geometry inspection (AICON 3D SYSTEMS GMBH 2018), are circular markers (e.g. described by NAIMARK & FOXLIN 2002) providing one reference point per marker. Other markers, like the squared ArUco markers (ROMERO RAMIREZ et al. 2018), provide four reference points per marker, one per corner. Reference points with known 3D object coordinates and corresponding 2D image coordinates allow estimating the camera pose relative to the markers by solving the perspectiven-point problem, for example. In the case markers are representing a charging platform, the relative pose between the camera-carrying vehicle and the platform can be obtained. Regardless of the camera position at the vehicle, markers attached to the ground will become invisible in images when the vehicle is close to the platform. In such situations, marker-based pose estimation becomes impossible. Nevertheless, algorithms like structure-from-motion (e.g. FITZGIBBON & ZISSERMAN 1998), visual SLAM (e.g. LEMAIRE et al. 2007) or visual odometry (e.g. NISTER et al. 2004) allow for vehicle pose estimation also in such situations (HANEL & STILLA 2017). Therefore, typically markerless image features like SIFT (LOWE 1999), SURF (BAY et al. 2008) or ORB (RUBLEE et al. 2011) are used to obtain matches between images and to create a map of the environment around the vehicle. Consequently, pose estimation becomes possible for images taken in mapped parts of the environment. However, estimated poses are not directly related to the charging platform. In this paper, a method for iteratively estimating the vehicle pose during an approach manoeuvre to a charging platform using images from a front-looking on-board camera is proposed, which can A. Hanel, P. Sudi, S. Pfenninger, E. Steinbach & U. Stilla 56 be used for both cases that markers representing the charging platform can be detected in the acquired images or not. The key contribution of this paper is the evaluation of the performance of the proposed method for different vehicle motion models, for the cases that special markers have been detected or not and for different filtering methods for data fusion between poses obtained using markers and poses obtained without seeing markers on simulated data of trajectories of typical parking manoeuvers. 2 Method for vehicle pose estimation during approach manoeuvres to a charging platform Objective of the method proposed in this paper is to estimate the vehicle pose relative to a charging platform iteratively during an approach manoeuvre to the platform using images taken with a frontlooking on-board camera. It is assumed that the charging platform is located in a parking lot of a parking garage or another place, where the beginning of the approach manoeuvre is known, for example by the known entry point to the parking garage. It is further assumed that the charging platform is represented by special markers attached to the ground, which can be detected in images and used for estimating the vehicle pose. By fusing vehicle dynamics data (e.g. vehicle position or vehicle velocity, depending on the dynamics model) obtained independently by pose estimation using special markers with vehicle dynamics data obtained by pose estimation using image features, the proposed method allows to estimate the vehicle pose even when the vehicle is close to the charging platform and the markers are not visible in the camera images. The algorithm for pose estimation consists of the following two steps, which are supposed to be performed iteratively for every image i acquired with the on-board camera during an approach manoeuvre. In the first step, depending on whether a sufficient number of markers has been detected, either stage 1 or stage 2 is performed for obtaining vehicle dynamics data: Stage 1 (Fig. 1, sufficient number of markers detected): Vehicle position estimates x and y are obtained as vehicle dynamics data from pose estimation using markers on the charging platform (“MB method”). Independently, vehicle velocity estimates v and v are obtained as dynamics data from pose estimation using a markerless method like SLAM or structure-from-motion (“ML method”) relying on image features. Stage 2 (Fig. 2, not sufficient number of markers detected): No position estimates are obtained by the MB method as no markers are visible, vehicle velocity estimates are Fig. 1: Stage one workflow for vehicle pose estimation with markers and markerless image features assuming that the charging platform is visible in the acquired on-board camera images. Dreiländertagung der DGPF, der OVG und der SGPF in Wien, Österreich – Publikationen der DGPF, Band 28, 2019 57 obtained by the ML method as in stage 1. In the second step, mutual vehicle pose estimates acco