Visual SLAM uses visual information, typically point features, to localise a camera and, at the same time, map the environment. In recent years, there has been interest in using scene-understanding capabilities to enhance the mapping process and object-level SLAM systems have appeared in response. However, most of the previous work is limited to prestored object models or pre-trained networks to represent the objects, which limits working scenarios or uses representations with limited scope, such as cubes or quadrics. To address this, we propose to use superquadrics as the object representation and, in this paper, present a proof of principle SLAM system in which object-based mapping is fully integrated with camera tracking via keyframe optimisation. The system was tested on simulated and real datasets, and the results show that the system can achieve lightweight and comparatively good object representation whilst also giving good camera trajectories estimates under certain scenarios.

We present a purely vision based geolocation method for aircraft flying over urban and suburban environments. The method is based on matching aerial images with geolocated map tiles using a shared low dimensional embedded space of descriptors. The Euclidean distance between descriptors is used as a similarity measure between domains. The similarity between the observation and map locations is then integrated with visual odometry to track the aircraft’s position and yaw using a particle filter. Furthermore, we propose an efficient method to generate map descriptors in testing time based on interpolation, allowing compact representation of large areas giving the potential for high levels of scalability. We experimented in different cities with areas above 20 km2in size and preliminary results based on a database of aerial imagery demonstrate that the method gives good results.

The ability to localise is key for robot navigation. We describe an efficient method for vision-based localisation, which combines sequential Monte Carlo tracking with matching ground-level images to 2-D cartographic maps such as OpenStreetMaps. The matching is based on a learned embedded space representation linking images and map tiles, encoding the common semantic information present in both and providing potential for invariance to changing conditions. Moreover, the compactness of 2-D maps supports scalability. This contrasts with the majority of previous approaches based on matching with single-shot geo-referenced images or 3-D reconstructions. We present experiments using the StreetLearn and Oxford RobotCar datasets and demonstrate that the method is highly effective, giving high accuracy and fast convergence.