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3dmapslam

Creating a segmented 3D octomap of the surroundings using ORB-SLAM3, with the possibility of removing or adding specific segmented objects to the octomap.

aanet

AANet: Adaptive Aggregation Network for Efficient Stereo Matching, CVPR 2020

anynet

Anytime Depth Estimation

auto-birds-eye

Bird's eye/Top Down view generation and mapping with deep learning.

city-surfaces

CitySurfaces semantic segmentation of sidewalk surfaces

ds-slam

gridsampler-onnxparser-trt-plugin

A sample for onnxparser working with trt user defined plugins for TRT7.0

image_projection

image_projection is a ROS package to create various projections from multiple calibrated cameras.

joint-freespace-detection

A Joint 3D-2D based Method for Free Space Detection on Roads

land

lanedet

An open source lane detection toolbox based on PyTorch, including SCNN, RESA, UFLD, LaneATT, CondLane, etc.

mmcv

OpenMMLab Computer Vision Foundation

modeld

Self driving car lane and path detection

mtcnn_facenet_cpp_tensorrt

Face Recognition on NVIDIA Jetson (Nano) using TensorRT

nice-slam

[CVPR'22] NICE-SLAM: Neural Implicit Scalable Encoding for SLAM

outdoor-navigation

real-time-self-adaptive-deep-stereo

Code for "Real-time self-adaptive deep stereo" - CVPR 2019 (ORAL)

revo_robot

Code for EDT's IGVC entry, Revo.

rfnet

RFNet: Real-time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection of Road-driving Images

ros-rover

How to build a one-armed, self-driving, University Rover Challenge robot with ROS

ros_deep_learning

Deep learning inference nodes for ROS / ROS2 with support for NVIDIA Jetson and TensorRT

segmentationmapping

self-driving-golf-cart

Be Driven 🚘

senior-project

snpe

Snapdragon Neural Processing Engine (SNPE) SDKThe Snapdragon Neural Processing Engine (SNPE) is a Qualcomm Snapdragon software accelerated runtime for the execution of deep neural networks. With SNPE, users can: Execute an arbitrarily deep neural network Execute the network on the SnapdragonTM CPU, the AdrenoTM GPU or the HexagonTM DSP. Debug the network execution on x86 Ubuntu Linux Convert Caffe, Caffe2, ONNXTM and TensorFlowTM models to a SNPE Deep Learning Container (DLC) file Quantize DLC files to 8 bit fixed point for running on the Hexagon DSP Debug and analyze the performance of the network with SNPE tools Integrate a network into applications and other code via C++ or Java

supervisely

AI for everyone! :tada: Neural networks, tools and a library we use in Supervisely

tensorrt-utils

⚡ Useful scripts when using TensorRT

ultrafast-lane-detection-inference-pytorch-

Example scripts for the detection of lanes using the ultra fast lane detection model in Pytorch.

urc

waypoint_nav

This package performs outdoor GPS waypoint navigation using move_base, robot_localization, gmapping and others. It can navigate while building a map, avoiding obstacles, and can navigate continuously between each goal.