This paper presents a preliminary conceptual investigation into an environment representation that has constant space complexity with respect to the camera image space. This type of representation allows the planning algorithms of a mobile agent to bypass what are often complex and noisy transformations between camera image space and Euclidean space. The approach is to compute per-pixel potential values directly from processed camera data, which results in a discrete potential field that has constant space complexity with respect to the image plane. This can enable planning and control algorithms, whose complexity often depends on the size of the environment representation, to be defined with constant run-time. This type of approach can be particularly useful for platforms with strict resource constraints, such as embedded and real-time systems.
In order to define values for the potential fields, this approach draws on a wealth of related works in optical flow and monocular collision avoidance, notably @cite_16 @cite_32 @cite_5 @cite_3 @cite_27 @cite_22 @cite_14 @cite_2 @cite_40 @cite_11 @cite_3 @cite_5 . The intuition of these approaches is that the information contained in a sequence of monocular stills provides sufficient information to compute time-to-contact (Definition ), which informs an agent about the rate of change of object proximity. The primary contribution of this work is a sensor-inspired representation space and algebra for enabling planning and control algorithms to reason effectively and efficiently with the output of this class of perception algorithms.
@cite_14 @cite_22 @cite_32 @cite_3 @cite_27 @cite_40 @cite_2 @cite_5 @cite_16 @cite_11
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The lure of using motion vision as a fundamental element in the perception of space drives this effort to use flow features as the sole cues for robot mobility. Real-time estimates of image flow and flow divergence provide the robot's sense of space. The robot steers down a conceptual corridor comparing left and right peripheral flows. Large central flow divergence warns the robot of impending collisions at "dead ends." When this occurs, the robot turns around and resumes wandering. Behavior is generated by directly using flow-based information in the 2D image sequence; no 3D reconstruction is attempted. Active mechanical gate stabilization simplifies the visual interpretation problems by reducing camera rotation. By combining corridor following and dead-end deflection, the robot has wandered around the lab at 30 cm s for as long as 20 minutes without collision. The ability to support this behavior in real-time with current equipment promises expanded capabilities as computational power increases in the future. > Computer-vision based collision risk assessment is important in collision detection and obstacle avoidance tasks. We present an approach to determine both time to collision (TTC) and collision risk for semi-rigid obstacles from videos obtained with an uncalibrated camera. TTC for a body moving relative to the camera can be calculated using the ratio of its image size and its time derivative. In order to compute this ratio, we utilize the local scale change and motion information obtained from detection and tracking of feature points, wherein lies the chief novelty of our approach. Using the same local scale change and motion information, we also propose a measure of collision risk for obstacles moving along different trajectories relative to the camera optical axis. Using videos of pedestrians captured in a controlled experimental setup, in which ground truth can be established, we demonstrate the accuracy of our TTC and collision risk estimation approach for different walking trajectories. Time to Contact (TTC) is a biologically inspired method for obstacle detection and reactive control of motion that does not require scene reconstruction or 3D depth estimation. TTC is a measure of distance expressed in time units. Our results show that TTC can be used to provide reactive obstacle avoidance for local navigation. In this paper we describe the principles of time to contact and show how time to contact can be measured from the rate of change of size of features. We show an algorithm for steering a vehicle using TTC to avoid obstacles while approaching a goal. We present the results of experiments for obstacle avoidance using TTC in static and dynamic environments. Obstacle avoidance is desirable for lightweight micro aerial vehicles and is a challenging problem since the payload constraints only permit monocular cameras and obstacles cannot be directly observed. Depth can however be inferred based on various cues in the image. Prior work has examined optical flow, and perspective cues, however these methods cannot handle frontal obstacles well. In this paper we examine the problem of detecting obstacles right in front of the vehicle. We developed a method to detect relative size changes of image patches that is able to detect size changes in the absence of optical flow. The method uses SURF feature matches in combination with template matching to compare relative obstacle sizes with different image spacing. We present results from our algorithm in autonomous flight tests on a small quadrotor. We are able to detect obstacles with a frame-to-frame enlargement of 120 with a high confidence and confirmed our algorithm in 20 successful flight experiments. In future work, we will improve the control algorithms to avoid more complicated obstacle configurations. This paper presents a novel two-frame motion estimation algorithm. The first step is to approximate each neighborhood of both frames by quadratic polynomials, which can be done efficiently using the polynomial expansion transform. From observing how an exact polynomial transforms under translation a method to estimate displacement fields from the polynomial expansion coefficients is derived and after a series of refinements leads to a robust algorithm. Evaluation on the Yosemite sequence shows good results. Collision detection and estimation from a monocular visual sensor is an important enabling technology for safe navigation of small or micro air vehicles in near earth flight. In this paper, we introduce a new approach called expansion segmentation, which simultaneously detects “collision danger regions” of significant positive divergence in inertial aided video, and estimates maximum likelihood time to collision (TTC) in a correspondenceless framework within the danger regions. This approach was motivated from a literature review which showed that existing approaches make strong assumptions about scene structure or camera motion, or pose collision detection without determining obstacle boundaries, both of which limit the operational envelope of a deployable system. Expansion segmentation is based on a new formulation of 6-DOF inertial aided TTC estimation, and a new derivation of a first order TTC uncertainty model due to subpixel quantization error and epipolar geometry uncertainty. Proof of concept results are shown in a custom designed urban flight simulator and on operational flight data from a small air vehicle. This paper proposes a new reactive method of avoiding dynamic obstacles by real-time optimization. A mobile robot is equipped with a monocular camera and dynamic obstacles are identified in the image sequence by clustering the optical flow. The respective epipoles of the clusters are determined and afterwards the relative epipole positions are evaluated to identify colliding and dangerous objects. In this work the correlation between the vehicle's velocities and the cluster epipoles is derived and utilized in the proposed cost function for shifting the epipoles. By optimizing this cost function the vehicle is able to avoid collisions, which means that the 3D motion is deduced from purely 2D image data. Finally, the validity of the concept is confirmed by hardware-in-the-loop simulations.