Pipeline Landmark Detection for Autonomous Robot Navigation using Time-of-Flight Imagery

The slight jump in distance measurements around 14.5 seconds can be attributed to the feature tracker switching from cone model to cylinder model, and a lack of reliable range data

We do this by mapping the data from the camera coordinate system into an image h o ( x, y ), which represents a view of the pipeline along the pipe axis, with ( x, y )

The coordination control problem where a follower robot is given the task to monitor a leader robot with a camera was efficiently solved using pseudoinverse redundancy

When using the graphics card pipeline a new image based tool is available for collision detection: depth map from appropriate point of views allow us to detect when a cloth particle

Figure 2: Landmark detection procedure for facial region localization: (a) Shape Index extrema; (b) Spin Image clas- sification; (c) Consistent landmark sets; (d) Best landmark set;

Human body dimension recognition was achieved by using a landmark detection based approach using both two 2D and 3D cameras for front and profile images.. The human

Enlarging the camera aperture angle increases the time that each image token is present in the picture, thereby allowing more measurements of each landmark, which should improve

The algorithm consists of the following main steps: 1) dark spot detection based on segmen- tation of the SAR image, 2) feature extraction from the segmented image, 3) classification