What is ego motion estimation?
In the field of computer vision, egomotion refers to estimating a camera’s motion relative to a rigid scene. An example of egomotion estimation would be estimating a car’s moving position relative to lines on the road or street signs being observed from the car itself.
What is Visual-inertial Slam?
Visual-inertial simultaneous localization and mapping (VI-SLAM) that fuses camera and IMU data for localization and environmental perception has become increasingly popular for several reasons. VINS-mono is a real-time optimization-based VI-SLAM system that uses a sliding window to provide high-precision odometry.
What is an ego vehicle?
Definition: Subject connected and/or automated vehicle, the behaviour of which is of primary interest in testing, trialling or operational scenarios. NOTE: Ego vehicle is used interchangeably with subject vehicle and vehicle under test (VUT).
What is an ego lane?
ELAS processes a temporal sequence of images, analyzing each of them individually. These images are expected to come from a monocular forward-looking camera mounted on a vehicle.
How to calculate camera motion using optical flow?
When trucking, all objects in the scene move at the same speed. Whereas, while panning objects that are closer to the camera move faster than those that are further. So the idea is to compare how different velocity is for different objects on the video. Here’s my approach step-by-step:
Are there any recent approaches to motion estimation?
We also briefly discuss more recent approaches using deep learning and promising future directions.
Which is the best definition of optical flow?
Let us begin with a high-level understanding of optical flow. Optical flow is the motion of objects between consecutive frames of sequence, caused by the relative movement between the object and camera. The problem of optical flow may be expressed as:
How are extracted features passed in optical flow?
The extracted features are passed in the optical flow function from frame to frame to ensure that the same points are being tracked. There are various implementations of sparse optical flow, including the Lucas–Kanade method, the Horn–Schunck method, the Buxton–Buxton method, and more.