Decision making for obstacle avoidance in autonomous mobile robots by time to contact and optical flow

Abstract

Vision is a vital cue for human navigation, and it has become in a powerful tool for robot navigation. Computer vision is useful for recognition of positional relationship and relative motion between themselves and objects in the environment. So, we address the problem of navigation for mobile robot in indoor environment. To this task, we tackle the problem using monocular vision, i.e., without other kind of sensors. Insects and some animals use tools as optical flow estimations and time to contact, to their navigation [1]. So, in this paper, we propose a method for obstacle avoidance, without constantly calculating the optical flow field, only it is calculated when the robot is close to colliding with an obstacle, and so, it uses the flow field divergence to decide which direction will should be taken. Physical experiments using a real robot have been conducted in unknown environments.

The Obstacle Avoidance for UAV Based on Improved Frames Difference and Optical Flow

“…For the minimize error, calculating the extremum of E 0 E h    (16) After abbreviation, h is written as:…”

A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm

“…The concept of time-to-collision (TTC) is important for collision alerting/avoidance and finds applications in robot navigation [ 1 , 2 ], mobility assistive devices for visually impaired people [ 3 , 4 ], and driving safety [ 5 ]. The main advantage of using TTC is its relative simplicity.…”

“…The main advantage of using TTC is its relative simplicity. Unlike in collision warning systems based on range sensors (radar, laser, structured-light depth sensor), or stereo cameras that measure the distance to the obstacles by binocular matching, TTC computation, requiring only a monocular camera, can be done without knowing the physical distance to the objects [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Therefore, low cost smart video sensors for collision avoidance can be made feasible by employing TTC-based approaches.…”

“…Existing TTC estimation approaches can be classified into three broad categories: feature-based (inferring TTC from the change of obstacle scale with sparse feature tracking or matching) [ 1 , 3 , 4 , 5 , 6 , 7 , 8 ], gradient-based (direct estimation from image intensity or spatiotemporal gradients) [ 2 , 9 , 10 , 11 ], and dense optical flow-based [ 12 , 13 , 14 , 15 ]. From the perspective of deployment on smart video sensors for our application, feature-based algorithms are not very suitable for efficient hardware implementation.…”