Abstract
Abstract. Objects follow designated path on maps, such as vehicles travelling on a road. This observation signifies topological representation of objects’ motion on the map. Considering the position of object is unknown initially, as it traverses the map by moving and turning, the spatial uncertainty of its whereabouts reduces to a single location as the motion trajectory would fit only to a certain map trajectory. Inspired by this observation, we propose a novel end-to-end localization approach based on topological maps that exploits the object motion and learning the map using an recurrent neural network (RNN) model. The core of the proposed method is to learn potential motion patterns from the map and perform trajectory classification in the map’s edge-space. Two different trajectory representations, namely angle representation and augmented angle representation (incorporates distance traversed) are considered and an RNN is trained from the map for each representation to compare their performances. The localization accuracy in the tested map for the angle and augmented angle representations are 90.43% and 96.22% respectively. The results from the actual visual-inertial odometry have shown that the proposed approach is able to learn the map and localize objects based on their motion.
Highlights
Without the loss of generality, object localization can be defined as finding where an object is on a map given sensory data
Despite limited test examples, we observe that the augmented trajectory representation obtained more robust localization performance than pure significant turning angle based on the training loss, confusion matrix and test results
We introduce a map learning approach and propose a motion based localization using recurrent neural network that can be employed in Global Positioning System (GPS) contested environments and is independent of the sensors used to generate motion trajectories
Summary
Without the loss of generality, object localization can be defined as finding where an object is on a map given sensory data. Despite advances in the field, localization is still a challenging problem especially when Global Positioning System (GPS) data is contested, such as degraded or not available. The limitations are notable: GPS is not accurate for especially civilian and consumer applications, and the signal may be unavailable or unreliable in serveral areas, such as underground, in tunnels, indoors and urban canyons. To resolve these limitations, researchers have proposed Indoor Positioning Systems (IPS) (Mautz, 2012) to localize objects using installed infrastructure, such as WiFi, Bluetooth, Ultra Wide Band (UWB), etc. Despite being a fast growing research area, IPS technology using external signals requires large investment and has high maintenance cost
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