Fig. 2

(A) An animal only moving forward removes the ambiguity in local flow templates so that both global and local errors indicate an away-from-foci advantage. Histograms on the left indicate the distribution of median absolute self-motion estimation errors for the best 50%, worst 50%, and full field global samples at matched resolution, and the best, worst, and median local regions over 1,000 simulations of 1,000 self-motion trajectories. Heatmaps indicating median errors are plotted in the same format as Figure 1. All model parameters are the same as Figure 1, except self-motion components other than V_Z were all set to zero and a 1 DOF estimation applied. For the local optic flow, both the best and median regions are associated with very low error, resulting in a high degree of histogram overlap.

(B) An animal translating and rotating in a plane sees higher errors and expanded regions of ambiguity. All model parameters are the same as Figure 1, except self-motion components other than V_Z, V_X, and ω_Y were all set to zero and a 3 DOF estimation applied.

(C) When noise varies linearly from none at 90° elevation to a maximum at −90° elevation, low error regions are shifted upward. All model parameters are otherwise the same as (B), and maximum noise had a standard deviation of 0.25 (relative to a maximal per-component flow magnitude of 1).

(D) When the environment is a floor plane rather than a sphere, V_Z error explodes at and above the equator. Heading estimates remove the depth ambiguity in translation component magnitude by comparing V_X and V_Z estimates, eliminating azimuth dependence for a pure lower field bias. All model parameters are the same as (B) except for the scene geometry, which is modeled as a flat floor 1m below and an infinitely far away wall in the upper visual field. Note that histograms and heatmaps for local errors in this figure are all on a log scale to account for the larger variability associated with different environmental settings.