ESANet: Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis
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Updated
Oct 24, 2023 - Python
ESANet: Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis
[CVPR 2023 Highlight] Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision
Implementation of Q-learning algorithm and Feedback control for the mobile robot (turtlebot3_burger) in ROS.
EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments
A repository of introductory autonomous ground vehicle (i.e., wheeled mobile robot) simulation examples in Python.
Learning human-aware robot navigation behavior from demonstrations via Maximum Entropy Inverse Reinforcement Learning.
Semantic Segmentation of Images and Point Clouds for Traversability Estimation
Open-AI Gym extension for robotics based on V-REP
Efficient and Robust Semantic Mapping for Indoor Environments
Mobile robot control in V-REP using Deep Reinforcement Learning Algorithms.
Autonomous search and retrieval of samples of interest on a simulated Martian terrain
A Python package to segment cluttered 2D floor plans based on down-sampling.
Code to prepare and use common datasets for scene analysis tasks
InEKF Localization and Semantic Mapping on the KITTI Dataset
Implementation of Least Squares Graph Optimization algorithm for graph-based SLAM.
Introduction to Mobile Robotics (engl.) - Autonomous Mobile Systems
Alfred is an open-source UGV platform built at IIIT-Delhi capable of emulating a full self-driving vehicle. Alfred is equipped with Intel NUC 12 Pro, NVIDIA AGX Orin, Intel Realsense D455, and Velodyne VLP-16 LiDAR, facilitating comprehensive evaluations and development state-of-the-art algorithms for mobile robotics.
The source files accompanying our research paper titled - "A Robust Approach for Improving the Accuracy of IMU based Indoor Mobile Robot Localization".
Developed and implemented 2D and 3D Pose Graph SLAM using the GTSAM library and Gauss Newton Solver on the Intel and Parking Garage g2o datasets respectively
A ROS package of an accepted paper in IEEE Robotics Automation and Letters (RA-L)
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