Cooperative Localisation Using Image Sensors in a Dynamic Traffic Scenario

Abstract

Abstract. Localisation is one of the key elements in navigation. Especially due to the development in automated driving, precise and reliable localisation becomes essential. In this paper, we report on different cooperation approaches in visual localisation with two vehicles driving in a convoy formation. Each vehicle is equipped with a multi-sensor platform consisting of front-facing stereo cameras and a global navigation satellite system (GNSS) receiver. In the first approach, the GNSS signals are used as excentric observations for the projection centres of the cameras in a bundle adjustment, whereas the second approach uses markers on the front vehicle as dynamic ground control points (GCPs). As the platforms are moving and data acquisition is not synchronised, we use time dependent platform poses. These time dependent poses are represented by trajectories consisting of multiple 6 Degree of Freedom (DoF) anchor points between which linear interpolation takes place. In order to investigate the developed approach experimentally, in particular the potential of dynamic GCPs, we captured data using two platforms driving on a public road at normal speed. As a baseline, we determine the localisation parameters of one platform using only data of that platform. We then compute a solution based on image and GNSS data from both platforms. In a third scenario, the front platform is used as a dynamic GCP which can be related to the trailing platform by markers observed in the images acquired by the latter. We show that both cooperative approaches lead to significant improvements in the precision of the poses of the anchor points after bundle adjustment compared to the baseline. The improvement achieved due to the inclusion of dynamic GCPs is somewhat smaller than the one due to relating the platforms by tie points. Finally, we show that for an individual vehicle, the use of dynamic GCPs can compensate for the lack of GNSS data.

Multi-Robot Cooperative Lidar Slam for Efficient Mapping in Urban Scenes

“…For unknown and unlocatable environments, simultaneous localization and mapping (SLAM) (Cadena et al, 2016)is a fundamental technology that can help the robot navigate and build maps in the environment. Multi-robot has the potential to be more effective and efficient than single-robot in some tasks such as localization and mapping (Huang et al, 2022;Trusheim et al, 2021;Xie et al, 2022). To accurately merge the map, the main goal of the multi-robot SLAM is to find the transformation between different robots.…”