Matching horizon features using a Messy Genetic Algorithm
Three line segment matching techniques are compared on horizon line matching problems. The application is to reduce camera angle uncertainty for outdoor robots. Each of the three algorithms seeks an optimal many-to-many match between a subset of 2D line segments extracted from a rendered terrain map and segments extracted from an image. Two algorithms are non-deterministic, with one performing Random Starts Local Search and the other using a Messy Genetic Algorithm. The third algorithm searches a list of ranked key feature matches using a limited deterministic local search. The Random Starts Local Search Algorithm is the slowest of the three algorithms and fails to find the best match on 9 out of 54 problems. In contrast, the Messy Genetic Algorithm correctly solves all 54 test problems. The Key Feature Algorithm is the fastest, but it fails to find the optimal match in three cases. The results suggest the Messy Genetic Algorithm is superior to the other two algorithms on problems of this type. The test imagery was collected on one of the Unmanned Ground Vehicle Program's vehicles operating at the Lockheed Martin Demo C test site. Using the optimal matches it is shown that uncertainty in camera pointing angle is reduced from several degrees to less than a degree.
messy genetic algorithm line segment matching robot orientation correction