Modeling of Unbounded Long-Range Drift in Visual Odometry

Visual odometry is a new navigation technology using video data. For long-range navigation, an intrinsic problem of visual odometry is the appearance of drift. The drift is caused by error accumulation, as visual odometry is based on relative measurements, and will grow unboundedly with time. The paper first reviews algorithms which adopt various methods to suppress this drift. However, as far as we know, no work has been done to statistically model and analyze the intrinsic properties of this drift. This paper uses an unbounded system model to represent the drift behavior of visual odometry. The model is composed of an unbounded deterministic part with unknown constant parameters, and a first-order Gauss-Markov process. A simple scheme is given to identify the unknown parameters as well as the statistics of the stochastic part from experimental data. Experiments and discussions are also provided.