Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios

Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high speed motions or in scenes characterized by high dynamic range. However, event cameras output only little information when the amount of motion is limited, such as in the case of almost still motion. Conversely, standard cameras provide instant and rich information about the environment most of the time (in low-speed and good lighting scenarios), but they fail severely in case of fast motions, or difficult lighting such as high dynamic range or low light scenes. In this paper, we present the first state estimation pipeline that leverages the complementary advantages of these two sensors by fusing in a tightly-coupled manner events, standard frames, and inertial measurements. We show on the publicly available Event Camera Dataset that our hybrid pipeline leads to an accuracy improvement of 130% over event-only pipelines, and 85% over standard-frames-only visual-inertial systems, while still being computationally tractable. Furthermore, we use our pipeline to demonstrate - to the best of our knowledge - the first autonomous quadrotor flight using an event camera for state estimation, unlocking flight scenarios that were not reachable with traditional visual-inertial odometry, such as low-light environments and high-dynamic range scenes.Comment: 8 pages, 9 figures, 2 table

Implementation of the autonomous functionalities on an electric vehicle platform for research and education

Self-driving cars have recently captured the attention of researchers and car manufacturing markets. Depending upon the level of autonomy, the cars are made capable of traversing from one point to another autonomously. In order to achieve this, sophisticated sensors need to be utilized. A complex set of algorithms is required to use the sensors data in order to navigate the vehicle along the desired trajectory. Polaris is an electric vehicle platform provided for research and education purposes at Aalto University. The primary focus of the thesis was to utilize all the sensors provided in Polaris to their full potential. So that, essential data from each sensor is made available to be further utilized either by a specific automation algorithm or by some mapping routine. For any autonomous robotic system, the first step towards automation is localization. That is to determine the current position of the robot in a given environment. Different sensors mounted over the platform provide such measurements in different frames of reference. The thesis utilizes the GPS based localization solution combined with the LiDAR data and wheel odometry to perform autonomous tasks. Robot Operating System is used as the software development tool in thesis work. Autonomous tasks include the determination of the global as well as the local trajectories. The endpoints of the global trajectories are dictated by the set of predefined GPS waypoints. This is called target-point navigation. A path needs to be planned that avoids all the obstacles. Based on the planned path, a set of velocity commands are issued by the embedded controller. The velocity commands are then fed to the actuators to move the vehicle along the planned trajectory

Cultural background shapes spatial reference frame proclivity

Spatial navigation is an essential human skill that is influenced by several factors. The present study investigates how gender, age, and cultural background account for differences in reference frame proclivity and performance in a virtual navigation task. Using an online navigation study, we recorded reaction times, error rates (confusion of turning axis), and reference frame proclivity (egocentric vs. allocentric reference frame) of 1823 participants. Reaction times significantly varied with gender and age, but were only marginally influenced by the cultural background of participants. Error rates were in line with these results and exhibited a significant influence of gender and culture, but not age. Participants cultural background significantly influenced reference frame selection; the majority of North-Americans preferred an allocentric strategy, while Latin-Americans preferred an egocentric navigation strategy. European and Asian groups were in between these two extremes. Neither the factor of age nor the factor of gender had a direct impact on participants navigation strategies. The strong effects of cultural background on navigation strategies without the influence of gender or age underlines the importance of socialized spatial cognitive processes and argues for socio-economic analysis in studies investigating human navigation

Cultural background shapes spatial reference frame proclivity

Spatial navigation is an essential human skill that is influenced by several factors. The present study investigates how gender, age, and cultural background account for differences in reference frame proclivity and performance in a virtual navigation task. Using an online navigation study, we recorded reaction times, error rates (confusion of turning axis), and reference frame proclivity (egocentric vs. allocentric reference frame) of 1823 participants. Reaction times significantly varied with gender and age, but were only marginally influenced by the cultural background of participants. Error rates were in line with these results and exhibited a significant influence of gender and culture, but not age. Participants’ cultural background significantly influenced reference frame selection; the majority of North-Americans preferred an allocentric strategy, while Latin-Americans preferred an egocentric navigation strategy. European and Asian groups were in between these two extremes. Neither the factor of age nor the factor of gender had a direct impact on participants’ navigation strategies. The strong effects of cultural background on navigation strategies without the influence of gender or age underlines the importance of socialized spatial cognitive processes and argues for socio-economic analysis in studies investigating human navigation.This work was funded by the European research grant: ERC- 2010-AdG #269716 – MULTISENSE, together with the Cognition and Neuroergonomics/Collaborative Technology Alliance #W911NF-10-2-0022

Brain Dynamics of Spatial Reference Frame Proclivity in Active Navigation.

Recent research into navigation strategy of different spatial reference frames (self-centered egocentric reference frame and environment-centered allocentric reference frame) has revealed that the parietal cortex plays an important role in processing allocentric information to provide a translation function between egocentric and allocentric spatial reference frames. However, most studies merely focused on a passive experimental environment, which is not truly representative of our daily spatial learning/navigation tasks. This study investigated the factor associated with brain dynamics that causes people to switch their preferred spatial strategy in both active and passive navigations to bridge the gap. Virtual reality (VR) technique and Omni treadmill are applied to realize actively walking for active navigation, and for passive navigation, participants were sitting while conducting the same task. Electroencephalography (EEG) signals were recorded to monitor spectral perturbations on transitions between egocentric and allocentric frames during a path integration task. Forty-one right-handed male participants from authors' university participated this study. Our brain dynamics results showed navigation involved areas including the parietal cortex with modulation in the alpha band, the occipital cortex with beta and low gamma band perturbations, and the frontal cortex with theta perturbation. Differences were found between two different turning-angle paths in the alpha band in parietal cluster event-related spectral perturbations (ERSPs). In small turning-angle paths, allocentric participants showed stronger alpha desynchronization than egocentric participants; in large turning-angle paths, participants for two reference frames had a smaller difference in the alpha frequency band. Behavior results of homing errors also corresponded to brain dynamic results, indicating that a larger angle path caused the allocentric to have a higher tendency to become egocentric navigators in the active navigation environment