Any Way You Look at It: Semantic Crossview Localization and Mapping With LiDAR

Abstract

Currently, GPS is by far the most popular global localization method. However, it is not always reliable or accurate in all environments. SLAM methods enable local state estimation but provide no means of registering the local map to a global one, which can be important for inter-robot collaboration or human interaction. In this work, we present a real-time method for utilizing semantics to globally localize a robot using only egocentric 3D semantically labelled LiDAR and IMU as well as top-down RGB images obtained from satellites or aerial robots. Additionally, as it runs, our method builds a globally registered, semantic map of the environment. We validate our method on KITTI as well as our own challenging datasets, and show better than 10 meter accuracy, a high degree of robustness, and the ability to estimate the scale of a top-down map on the fly if it is initially unknown.

Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors

“…Geo-tracking methods are applicable when the initial pose is known, and have demonstrated feasible results using input from visual cameras [10], range scanners [11], [12] and both [13]. Nevertheless, there are several limitations of current approaches to geo-tracking that we address in this work:…”

“…• Several methods [13], [14], [15], [16] train and test on data from the same city area. Others [17], [18] assume that the ground region or aerial data to be tested on has already been seen during the training stage.…”

“…• Some methods rely on specific environment features such as edges of buildings [19], [20], [14], [15], [16] or semantic features [13], [21]. This limits the applicability to locations where these features are present and visible, i.e.…”

CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map

“…Satellite image [21], [22], [23] is another world-wide 2D map for vehicle localization. Similar to the OpenStreetMap, satellite images do not contain 3D features, which limits its use in 3D localization.…”

“…Similar to the OpenStreetMap, satellite images do not contain 3D features, which limits its use in 3D localization. In [22], semantic segmentation is applied to the satellite images and compared with the LiDAR scannings to perform global vehicle localization.…”