Visual Loop Closure Detection for a Future Mars Science Helicopter

Future Mars Rotorcrafts will require the ability to precisely navigate to previously visited locations in order to return to a safe landing site or execute precise scientific measurements, such as sample acquisition or targeted sensing. To enable a future Mars Science Helicopter to perform in-flight loop closures, we present an on-board visual loop closure detection system based on a Bag-of-Words (BoW) approach that is efficient enough to run in real-time on the anticipated computationally constrained flight avionics. Our system establishes image-to-image associations between incoming images of a downward-looking navigation camera and previously observed geo-tagged keyframes stored in a database. The system extracts ORB features which are quantized into a BoW histogram using a custom one million words hierarchical vocabulary, trained on synthetic images from a Mars simulation. An efficient database query using an inverted index produces a set of candidate frames which we check for geometrical consistency. For efficient feature matching, we leverage the vocabulary to perform fast approximate nearest neighbor search. The geometrical check accepts loop closure pairs whose essential matrix is supported by a minimum number of feature matches, which are pre-selected based on a rotational consistency check. The vocabulary and the methods used for the geometrical consistency checks were chosen to maximize the performance while allowing single-thread real-time execution on a computationally constrained embedded processor. We demonstrate and evaluate the proposed system both on simulated and real-world data, including flight data from the Mars Helicopter
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