Feature Based Object-Related Navigation

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Goal of this research project is the development of a system for object-related navigation. Instead of following global trajectories in a previously built up map, the aim of the approach is to navigate relative to perceived objects ("Turn left on the third crossroad, pass a tree and stop before the second house on the right hand side"). Further, by incorporating object features (salient edges, textures, etc.) into navigation, we intend to increase the robustness with respect to measurement and model errors, in comparison to state-of-the-art approaches, that often utilize abstract bounding box models with artificial reference points only. By coupling navigation to perceived objects we aim on the one hand at quick reactions to unforseen events, like sudden lane changes of vehicles that drive on the neighbor lane, on the other hand at the generation of plans like: "Follow lane, overtake vecicle, ..." to lower the complexity of plan generation and alteration, especially in high dynamic environments.We intend to demonstrate the results of our research with our autonomous vehicle MuCAR-3. Based on already existing methods for object detection and classification in both, LIDAR and camera data, we plan to show how the new navigation approach copes with different scenarios in the real world, like overtaking maneuvers or turns on heavy crowded crossroads, to reach a predefined goal, like: "Follow object X". Occuring problem situations, like road blockages, have to be resolved autonomously. In a further research step, the vehicle shall explore unknown terrain to create a topological object-related map, with classified objects being its nodes. But instead of mapping the whole environment, the system aims at the comprehension of objects that are relevant for autonomous navigation only. The vehicle is intended to drive within this map in an object-related sense towards a user defined goal. To show the approach's efficiency we neglect the use of GPS or any global metric representation.