Pinned Repositories
RosBot
Autonomous Navigation Project using ROS
ROS-Basic
Basic ROS Course
cxxtest
CxxTest Unit Testing Framework
Data-Structure-Algorithm-Programs
This Repo consists of Data structures and Algorithms
LANE-DETECTION-USING-DEEP-LEARNING
Autonomous self-driving is in the trend for implementing it in our real life to remove all the hassles and accidents. Modern-day transport has come a long way but still far away from perfection and all-around safety. Lane Detection is a concept of demarcating lanes on the roads while the vehicle is moving. It has the capability of changing the vehicular movements on road, making them more organized and safe. This leap could provide for driver carelessness and avoid a lot of mishaps on the roads. Ride-hailing services like Uber and Ola can use them to monitor drivers and rate them based on driving skills. We have designed and trained a deep Convolutional Network model from scratch for lane detection since a CNN based model is known to work best for image datasets. We have used BDD100k dataset for training and testing for our model. We have used various metrics values for hyper-parameters tuning and took the ones which gave the best result. The training is done on Supercomputer NVIDIA-DGX V100. Idea By: Aditya Sharma, Microsoft
ROS-MQTT-Project
Integrating MQTT with ROS based IoT applications
RosBot
Autonomous Navigation Project using ROS
RosBot_extended
added Cartographer and Karto slam.
ROS-AD-SLAM-Evaluation
Summary - Comparative Study and Implementation of SLAM in ROS, Gazebo, CARLA and Prototype
pisers's Repositories
pisers/cxxtest
CxxTest Unit Testing Framework
pisers/Data-Structure-Algorithm-Programs
This Repo consists of Data structures and Algorithms
pisers/LANE-DETECTION-USING-DEEP-LEARNING
Autonomous self-driving is in the trend for implementing it in our real life to remove all the hassles and accidents. Modern-day transport has come a long way but still far away from perfection and all-around safety. Lane Detection is a concept of demarcating lanes on the roads while the vehicle is moving. It has the capability of changing the vehicular movements on road, making them more organized and safe. This leap could provide for driver carelessness and avoid a lot of mishaps on the roads. Ride-hailing services like Uber and Ola can use them to monitor drivers and rate them based on driving skills. We have designed and trained a deep Convolutional Network model from scratch for lane detection since a CNN based model is known to work best for image datasets. We have used BDD100k dataset for training and testing for our model. We have used various metrics values for hyper-parameters tuning and took the ones which gave the best result. The training is done on Supercomputer NVIDIA-DGX V100. Idea By: Aditya Sharma, Microsoft
pisers/ROS-MQTT-Project
Integrating MQTT with ROS based IoT applications
pisers/RosBot
Autonomous Navigation Project using ROS
pisers/RosBot_extended
added Cartographer and Karto slam.