Experimental Evaluation and Improvement of a Viewframe-Based Navigation Method

Abstract

Insects like ants and bees navigate robustly in their environments in spite of their small brains using vision as their primary sensor. Inspired by this, researchers at DLR are working on a range free navigation system using visual features. This ability is specially useful for autonomous navigation in large environments and also for computationally limited small robots. Each location in the environment is represented as a viewframe. A viewframe is a set of landmark observations where each landmark observation contains landmark I.D, descriptor and corresponding angle with respect to the robot’s location. Binary Robust Invariant Scalable Keypoints (BRISK) features extracted from the omnidirectional images were used as landmarks in this work. The environment is represented as a Trail-Map which preserves the relationship between adjacent viewframes and is efficient at both storing and pruning the map size when required. This work experimentally evaluates the current system and improves it. In this work, as an extension to the Trail-Map representation, topological knowledge was extracted with the help of dimensionality reduction techniques and by defining dissimilarity measures between any two viewframes. Using this topological knowledge, a pose graph is developed adding edges between viewframes based on how close they are in addition to the adjacency connections. With the help of this map, shorter paths were identified for homing. The topological mapping pipeline was implemented on the robot and experiments were performed in both indoor and outdoor environments. The performance of different dissimilarity measures and dimensionality reduction techniques in building a topological map of viewframes was evaluated. The experiments showed that using this pose graph representation, the robot could take shorter paths which are a subset of the long exploration paths by using the intersections of the paths.