Data Recording

The mobile robot used in the real world experiments is a Pioneer 3AT equipped with an omnidirectional imaging system on top (see Fig. 4.2). In the experiments different imaging system have been used. In the first real world experiment in section 5.1.2 the omnidirectional imaging system is made of a camera pointing at a chrome-colored plastic ellipsoid. For the experiments in section 5.2 where the performance of the SFA-model is compared to other methods a high quality omnidirectional vision system3 _{has been}

used. The trajectories were driven manually using a wireless joypad. Two notebooks with synchronized clocks were used to collect the data during the experiments. One notebook, which was placed on the robot, saved the camera images together with the current timestamp and converted the signals received from the joypad to the correspond-

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30 4. Data Recording and Ground Truth Acquisition

ing motion commands. The second notebook was used to run the software for ground truth data acquisition based on the optical marker detection method described in the previous section. The pose of the robot was measured throughout the experiments and saved together with the current time stamp. In a post-processing step each image was assigned with a ground truth position using linear interpolation based on the timestamps. For the long-term experiments in section 6.2.2 we used a different robot platform equipped with a spherical lens camera4. The robot is able to autonomously follow a certain closed loop trajectory defined by a border wire. Since the robot begins and terminates opera- tion in a base station the exact position and orientation in the beginning and the end of the closed loop trajectory are known. This allows to detect accumulated errors and to correct the estimated trajectory by distributing the weighted error backwards along the trajectory. Therefore, the robot’s trajectory can be precisely reconstructed using wheel odometry and a gyroscope [33]. The resulting estimated trajectory is considered as ground truth information which is saved together with the current image to an at- tached storage device.

Example images from the different camera systems and specific details regarding the data recording will be given in the sections covering the respective experiments.

Figure 4.2: Pioneer 3AT equipped with an omnidirectional vision system and the marker-box for ground truth data acquisition.

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5 Self-localization

Slow Feature Analysis applied to the temporal sequence of visual input from a mobile robot during exploration of a certain environment yields representations of the robot’s position or orientation depending on the movement statistics. Using an omnidirectional camera allows to manipulate the perceived image statistics by simulating a full rotation for every captured image. After the unsupervised learning phase the resulting SFA func- tions ideally code for the position on the x- and y-axis and are invariant with respect to the orientation of the robot. In order to be useful for higher level tasks such as navi- gating to a certain location in the environment the quality of the learned representation has to be adequate. To quantify and visualize the encoded spatial information of the SFA-outputs in a metric way we compute a regression function from the SFA-outputs from a training run to the metric ground truth positions and subsequently apply it to SFA-outputs from a separate test run. The quality of the learned representations can then be assessed quantitatively by performing a self-localization task and measuring the metric accuracy w.r.t. the ground truth. Additionally, it allows to compute the sensitiv- ity of the SFA functions to the spatial position p := (x, y) given by the mean positional variance ηpand the sensitivity to the orientation ϕ, characterized by its mean orientation

variance ηϕ. Qualitative information about the learned slow feature representations can

be obtained by plotting the individual color coded SFA-outputs over every position in the training area. Ideally, these so called spatial firing maps show orthogonal gradients along the coordinate axis for the first two outputs.

In the first section the SFA-model is validated by applying it in a simulator and a real world experiment. The resulting SFA representations are analyzed w.r.t. the quality of the spatial coding and their orientation invariance. Section 5.2 compares the local- ization accuracy of the SFA-model to state of the art visual simultaneous localization and mapping (SLAM) methods in further indoor and outdoor environments. While the SFA-model estimates an absolute position from a single image other approaches usually incorporate ego motion information and incrementally build up their belief of the own position. In section 5.3 we present a method to combine odometry information with the SFA estimates in probabilistic filter. Therefore, we propose an unsupervised learn- ing approach to obtain the mapping from slow feature outputs to metric coordinates by imposing constraints on the trajectory and using odometry measurements. In the

32 5. Self-localization

last section an alternative model for SFA-localization is presented which learns spatial representations from single or multiple tracked landmark views1.

5.1 Validation of the Approach