Vehicle self-localization with high-precision digital maps

Abstract

Cooperative driver assistance functions benefit from sharing information on the local environments of individual road users by means of communication technology and advanced sensor data fusion methods. However, the consistent integration of environment models as well as the subsequent interpretation of traffic situations impose high requirements on the self-localization accuracy of vehicles. This paper presents methods and models for a map-based vehicle self-localization approach. Basically, information from the vehicular environment perception (using a monocular camera and laser scanner) is associated with data of a high-precision digital map in order to deduce the vehicle's position. Within the Monte-Carlo localization approach, the association of road markings is reduced to a prototype fitting problem which can be solved efficiently due to a map model based on smooth arc splines. Experiments on a rural road show that the localization approach reaches a global positioning accuracy in both lateral and longitudinal direction significantly below one meter and an orientation accuracy below one degree even at a speed up to 100 km/h in real-time.

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

“…Qin et al [20] investigate Monte Carlo vehicle localization in urban environments based on curb and intersection features. As demonstrated by the works of Schindler [21] and Schreiber et al [22], road markings as landmarks can also yield high localization accuracy. Hata and Wolf [23] feed both curb features and road markings to their particle filter.…”

Co‐registration of panoramic mobile mapping images and oblique aerial images

“…Real-time capable applications, such as simultaneous localisation and mapping (SLAM) or visual odometry, allow for the introduction of external references by map-aiding (Schindler, 2013;Gruyer et al, 2014;Gu et al, 2016;Roh et al, 2016) or shadow matching (Groves et al, 2013;Irish et al, 2015;Strode and Groves, 2016); the latter classifies GNSS signals with respect to their angle of incidence. It should be noted that there are a multitude of approaches which do not utilise external data, but rather optimise the trajectory using onboard sensor systems (Zhang and Singh, 2015;Balazadegan Sarvrood et al, 2016;Carlone and Karaman, 2017).…”

Fully Automatic Feature-Based Registration of Mobile Mapping and Aerial Nadir Images for Enabling the Adjustment of Mobile Platform Locations in GNSS-Denied Urban Environments

“…GNSS multipath effects, for instance, can be discarded or filtered by using shadow matching approaches which utilise 3D building models to detect GNSS signals with unlikely incident angles (Gu et al (2016); Strode and Groves (2016); Groves et al (2013)). In the research field of autonomous driving, many methods rely on lane marking detection in conjunction with a digital map to support the localisation task (Gruyer et al (2014), Schindler (2013), Roh et al (2016)). A related method is visual odometry where features are tracked across multiple frames of the sensor system installed on the platform to allow for a better relative positioning (Badino et al (2013), Zhang and Singh (2015)).…”