Redundant neural vision systems: competing for collision recognition roles

Ability to detect collisions is vital for future robots that interact with humans in complex visual environments. Lobula giant movement detectors (LGMD) and directional selective neurons (DSNs) are two types of identified neurons found in the visual pathways of insects such as locusts. Recent modelling studies showed that the LGMD or grouped DSNs could each be tuned for collision recognition. In both biological and artificial vision systems, however, which one should play the collision recognition role and the way the two types of specialized visual neurons could be functioning together are not clear. In this modeling study, we compared the competence of the LGMD and the DSNs, and also investigate the cooperation of the two neural vision systems for collision recognition via artificial evolution. We implemented three types of collision recognition neural subsystems – the LGMD, the DSNs and a hybrid system which combines the LGMD and the DSNs subsystems together, in each individual agent. A switch gene determines which of the three redundant neural subsystems plays the collision recognition role. We found that, in both robotics and driving environments, the LGMD was able to build up its ability for collision recognition quickly and robustly therefore reducing the chance of other types of neural networks to play the same role. The results suggest that the LGMD neural network could be the ideal model to be realized in hardware for collision recognition

Real time biologically-inspired depth maps from spherical flow

We present a strategy for generating real-time relative depth maps of an environment from optical flow, under general motion. We achieve this using an insect-inspired hemispherical fish-eye sensor with 190 degree FOV, and a de-rotated optical flow field. The de-rotation algorithm applied is based on the theoretical work of Nelson and Aloimonos (1988). From this we obtain the translational component of motion, and construct full relative depth maps on the sphere. We examine the robustness of this strategy in both simulation and real-world experiments, for a variety of environmental scenarios. To our knowledge, this is the first demonstrated implementation of the Nelson and Aloimonos algorithm working in real-time, over real image sequences. In addition, we apply this algorithm to the real-time recovery of full relative depth maps. These preliminary results demonstrate the feasibility of this approach for closed-loop control of a robot

The Role of Vision Algorithms for Micro Aerial Vehicles

This work investigates the research topics related to visual aerial navigation in loosely structured and cluttered environments. During the inspection of the desired infrastructure the robot is required to fly in an environment which is uncertain and only partially structured because, usually, no reliable layouts and drawings of the surroundings are available. To support these features, advanced cognitive capabilities are required, and in particular the role played by vision is of paramount importance. The use of vision and other onboard sensors such as IMU and GPS play a fundamental to provide high level degree of autonomy to flying vehicles. In detail, the outline of this thesis is organized as follows • Chapter 1 is a general introduction of the aerial robotic field, the quadrotor platform, the use of onboard sensors like cameras and IMU for autonomous navigation. A discussion about camera modeling, current state of art on vision based control, navigation, environment reconstruction and sensor fusion is presented. • Chapter 2 presents vision based control algorithms useful for reactive control like collision avoidance, perching and grasping tasks. Two main contributions are presented based on relative depth map and image based visual servoing respectively. • Chapter 3 discusses the use of vision algorithms for localization and mapping. Compared to the previous chapter, the vision algorithm is more complex involving vehicle’s poses estimation and environment reconstruction. An algorithm based on RGB-D sensors for localization, extendable to localization of multiple vehicles, is presented. Moreover, an environment representation for planning purposes, applied to industrial environments, is introduced. • Chapter 4 introduces the possibility to combine vision measurements and IMU to estimate the motion of the vehicle. A new contribution based on Pareto Optimization, which overcome classical Kalman filtering techniques, is presented. • Chapter 5 contains conclusion, remarks and proposals for possible developments

Enhancing 3D Visual Odometry with Single-Camera Stereo Omnidirectional Systems

We explore low-cost solutions for efficiently improving the 3D pose estimation problem of a single camera moving in an unfamiliar environment. The visual odometry (VO) task -- as it is called when using computer vision to estimate egomotion -- is of particular interest to mobile robots as well as humans with visual impairments. The payload capacity of small robots like micro-aerial vehicles (drones) requires the use of portable perception equipment, which is constrained by size, weight, energy consumption, and processing power. Using a single camera as the passive sensor for the VO task satisfies these requirements, and it motivates the proposed solutions presented in this thesis. To deliver the portability goal with a single off-the-shelf camera, we have taken two approaches: The first one, and the most extensively studied here, revolves around an unorthodox camera-mirrors configuration (catadioptrics) achieving a stereo omnidirectional system (SOS). The second approach relies on expanding the visual features from the scene into higher dimensionalities to track the pose of a conventional camera in a photogrammetric fashion. The first goal has many interdependent challenges, which we address as part of this thesis: SOS design, projection model, adequate calibration procedure, and application to VO. We show several practical advantages for the single-camera SOS due to its complete 360-degree stereo views, that other conventional 3D sensors lack due to their limited field of view. Since our omnidirectional stereo (omnistereo) views are captured by a single camera, a truly instantaneous pair of panoramic images is possible for 3D perception tasks. Finally, we address the VO problem as a direct multichannel tracking approach, which increases the pose estimation accuracy of the baseline method (i.e., using only grayscale or color information) under the photometric error minimization as the heart of the “direct” tracking algorithm. Currently, this solution has been tested on standard monocular cameras, but it could also be applied to an SOS. We believe the challenges that we attempted to solve have not been considered previously with the level of detail needed for successfully performing VO with a single camera as the ultimate goal in both real-life and simulated scenes