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emg-based-control-of-lower-limb-prosthesis-using-myo-armband

obstacle-avoidance-robot

Arduino code

ouster_example

Ouster, Inc. sample code

pedestrian-detection

Using the Raspberry Pi “Smart Video Car Kit our aim is to detect pedestrians and classify them as either being stationary or moving. Object detection will be done via R (Regional)-CNN. The data that will be used is the Penn-Fudan Database for Pedestrian Detection and Segmentation http://pascal.inrialpes.fr/data/human/. This data will be trained to see when pedestrians are stationary and when they are moving, and for testing we will classify the active status of pedestrians in real time. The robot will indicate if the pedestrians are active (moving) or inactive (stationary). R-CNN is a special type of CNN that focuses on the important parts of an image, rather than the image in its entirety.

sparse-depth-completion

Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI)