Holistic methods for visual navigation of mobile robots in outdoor environments

Differt D. Holistic methods for visual navigation of mobile robots in outdoor environments. Bielefeld: Universität Bielefeld; 2017

Spectral skyline separation: Extended landmark databases and panoramic imaging

Differt D, Möller R. Spectral skyline separation: Extended landmark databases and panoramic imaging. Sensors. 2016;16(10): 1614

The problem of home choice in skyline-based homing

Müller M, Bertrand O, Differt D, Egelhaaf M. The problem of home choice in skyline-based homing. PLOS One. 2018;13(3): e0194070.Navigation in cluttered environments is an important challenge for animals and robots alike and has been the subject of many studies trying to explain and mimic animal navigational abilities. However, the question of selecting an appropriate home location has, so far, received only little attention. This is surprising, since the choice of a home location might greatly influence an animal’s navigation performance. To address the question of home choice in cluttered environments, a systematic analysis of homing trajectories was per- formed by computer simulations using a skyline-based local homing method. Our analysis reveals that homing performance strongly depends on the location of the home in the envi- ronment. Furthermore, it appears that by assessing homing success in the immediate vicin- ity of the home, an animal might be able to predict its overall success in returning to it from within a much larger area

Real-time rotational image registration

Differt D. Real-time rotational image registration. In: Proceedings of the International Conference on Advanced Robotics (ICAR). IEEE; 2017: 1--6

Insect models of illumination-invariant skyline extraction from UV and green channels

Differt D, Möller R. Insect models of illumination-invariant skyline extraction from UV and green channels. Journal of Theoretical Biology. 2015;380(7):444-462

A generalized multi-snapshot model for 3D homing and route following

Differt D, Stuerzl W. A generalized multi-snapshot model for 3D homing and route following. Adaptive Behavior. 2021;29(6):531-548.Inspired by the learning walks of the ant Ocymyrmex robustior, the original multi-snapshot model was introduced, which-in contrast to the classical "single snapshot at the goal" model-collects multiple snapshots in the vicinity of the goal location that subsequently can be used for homing, that is, for guiding the return to the goal. In this study, we show that the multi-snapshot model can be generalized to homing in three dimensions. In addition to capturing snapshots at positions shifted in all three dimensions, we suggest to decouple the home direction from the orientation of snapshots and to associate a home vector with each snapshot. We then propose a modification of the multi-snapshot model for three-dimensional route following and evaluate its performance in an accurate reconstruction of a real environment. As an illumination-invariant alternative to grayscale images, we also examine sky-segmented images. We use spherical harmonics as efficient representation of panoramic images enabling low memory usage and fast similarity estimation of rotated images. The results show that our approach can steer an agent reliably along a route, making it also suitable for robotic applications using on-board computers with limited resources

Home locations grouped by their local pattern of convergence.

<p>Homing vectors converging on the home location (<i>red dot</i>) are shown in <i>blue</i>, non-converging ones are shown in <i>grey</i>. Points reachable from all surrounding grid nodes are called sinks (<b>a</b>), those reachable from some, but not all directions are called saddle points (<b>b</b>). Points not reachable from any direction are called sources (<b>c</b>). We call these local patterns of convergence a location’s <i>target type</i>.</p

The problem of home choice in skyline-based homing

<div><p>Navigation in cluttered environments is an important challenge for animals and robots alike and has been the subject of many studies trying to explain and mimic animal navigational abilities. However, the question of selecting an appropriate home location has, so far, received only little attention. This is surprising, since the choice of a home location might greatly influence an animal’s navigation performance. To address the question of home choice in cluttered environments, a systematic analysis of homing trajectories was performed by computer simulations using a skyline-based local homing method. Our analysis reveals that homing performance strongly depends on the location of the home in the environment. Furthermore, it appears that by assessing homing success in the immediate vicinity of the home, an animal might be able to predict its overall success in returning to it from within a much larger area.</p></div

Overview of the ASV homing method.

<p><b>Top row</b>: The <i>central panel</i> shows a top view of the simulated cluttered environment. Black boxes represent objects, the <b>blue</b> and <b>green</b> markers indicate the current and home location of the agent. To perform a homing run between these two locations, panoramic views are rendered (1) at each location (<i>left and right panel</i>) using the CyberInsect toolbox [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0194070#pone.0194070.ref036" target="_blank">36</a>]. <b>Bottom row</b>: Vector profiles of each scene are created (2) taking the given viewing direction as each vector’s argument and the relative pixel intensity of the corresponding image column as its norm. This represents the skyline of each scene (<i>left and right panel</i>). Summing (3) these vector profiles yields the Average Skyline Vector (ASV) of each scene <i>(central panel</i>, <b>blue</b> and <b>green</b> arrows). (4) Subtracting the ASVs yields the homing vector (HV, <i>central panel</i>, <b>red</b> arrow). The agent will follow this HV for a given distance until another HV is generated at its new location.</p