Airsim segmentation

Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals.

Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop HITL simulations with support for popular protocols e. The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols.

In addition, the modular design enables various components to be easily usable independently in other projects. We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights.

airsim segmentation

One of the key challenges with these techniques is the high sample complexity - the amount of training data needed to learn useful behaviors is often prohibitively high. This issue is further exacerbated by the fact that autonomous vehicles are often unsafe and expensive to operate during the training phase. In order to seamlessly operate in the real world the robot needs to transfer the learning it does in simulation.

Currently, this is a non-trivial task as simulated perception, environments and actuators are often simplistic and lack the richness or diversity of the real world. For example, for robots that aim to use computer vision in outdoor environments, it may be important to model real-world complex objects such as trees, roads, lakes, electric poles and houses along with rendering that includes finer details such as soft shadows, specular reflections, diffused inter-reflections and so on.

Similarly, it is important to develop more accurate models of system dynamics so that simulated behavior closely mimics the real-world. AirSim is an open-source platform AirSimGitHub that aims to narrow the gap between simulation and reality in order to aid development of autonomous vehicles. The platform seeks to positively influence development and testing of data-driven machine intelligence techniques such as reinforcement learning and deep learning.

It is inspired by several previous simulators see related workand one of our key goals is to build a community to push the state-of-the-art towards this goal. While an exhaustive review of currently used simulators is beyond the scope of this paper, we mention a few notable recent works that are closest to our setting and has deeply influenced this work. Gazebo koenigdesign has been one the most popular simulation platforms for the research work.

It has a modular design that allows to use different physics engines, sensor models and create 3D worlds. Gazebo goes beyond monolithic rigid body vehicles and can be used to simulate more general robots with links-and-joints architecture such as complex manipulator arms or biped robots.

While Gazebo is fairly feature rich it has been difficult to create large scale complex visually rich environments that are closer to the real world and it has lagged behind various advancements in rendering techniques made by platforms such as Unreal engine or Unity. Other notable efforts includes Hector meyercomprehensive that primarily focuses on tight integration with popular middleware ROS and Gazebo.

It offers wind tunnel tuned flight dynamics, sensor models that includes bias drift using Gaussian Markov process and software-in-loop using Orocos toolchain. However, Hector lacks support for popular hardware platforms such as Pixhawk and protocols such as MavLink.

Similarly, RotorS furrerrotors provides a modular framework to design Micro Aerial Vehicles, and build algorithms for control and state estimation that can be tested in simulator. RotorS also uses Gazebo as its platform, consequently limiting its perception related capabilities. Finally, jMavSim jmavsim is easy to use simulator that was designed with a goal of testing PX4 firmware and devices. It is therefore tightly coupled with PX4 simulation APIs, uses albeit simpler sensor models and utilizes simple rendering engine without any objects in the environment.

Apart from these, there have been many games like simulators and training applications, however, these are mostly commercial closed-source software with little or no public information on models, accuracy of simulation or development APIs for autonomous applications.

Our simulator follows a modular design with an emphasis on extensibility.

airsim segmentation

The typical setup for an autonomous aerial vehicle includes the flight controller firmware such as PX4 meierpixhawkROSFlight jacksonrosflightHackflight levyHackflight etc.Published: Feb 29, by Wenshan Wang. We present a challenging dataset, the TartanAir, for robot navigation task and more. The data is collected in photo-realistic simulation environments in the presence of various light conditions, weather and moving objects. By collecting data in simulation, we are able to obtain multi-modal sensor data and precise ground truth labels, including the stereo RGB image, depth image, segmentation, optical flow, camera poses, and LiDAR point cloud.

We set up a large number of environments with various styles and scenes, covering challenging viewpoints and diverse motion patterns, which are difficult to achieve by using physical data collection platforms.

In order to enable data collection in such large scale, we develop an automatic pipeline, including mapping, trajectory sampling, data processing, and data verification. We evaluate the impact of various factors on visual SLAM algorithms using our data.

Although we use the simulation, our goal is to push the limits of Visual SLAM algorithms in the real world by providing a challenging benchmark for testing new methods, as well as large diverse training data for learning-based methods. The dataset is published using Azure Open Dataset platform. Please contact wenshanw [at] andrew [dot] cmu [dot] edu for accessing the whole dataset. Impressive progress has been made with both geometric-based methods and learning-based methods.

However, developing robust and reliable SLAM methods for real-world applications is still a challenging problem. Real-life environments are full of difficult cases such as light changes or lack of illumination, dynamic objects, and texture-less scenes. We collect a large dataset using photo-realistic simulation environments.

We minimize the sim2real gap by utilizing a large number of environments with various styles and diverse scenes. A special goal of our dataset is to focus on the challenging environments with changing light conditions, adverse weather, and dynamic objects.

State-of-the-art SLAM algorithms are struggled in tracking the camera pose in our dataset and constantly getting lost on some challenging sequences. We propose a metric to evaluate the robustness of the algorithm. In addition, we develop an automatic data collection pipeline, which allows us to process more environments with minimum human intervention. We have adopted more than 50 photo-realistic simulation environments in the Unreal Engine.

The environments provide us a wide range of scenarios that cover many interesting yet challenging situations. The simulation scenes consist of. In each simulated environment, we gather data by following multiple routes and making movements with different levels of aggressiveness. The virtual camera can move slowly and smoothly without sudden jittering actions. Or it can have intensive and violent actions mixed with significant rolling and yaw motions.

By unleashing the power of the Unreal Engine and AirSimwe can extract various types of ground truth labels including depth, semantic segmentation tag, and camera pose. From the extracted raw data, we further compute other ground truth labels such as optical flow, stereo disparity, simulated multi-line LiDAR points, and simulated IMU readings. We develop a highly automated pipe-line to facilitate data acquisition.Since we have participated in more traditional robotics competitions than you can imagine, we wanted to do something special for our th prize.

As I had recently finished the CSN course, I felt really eager to apply machine learning algorithms to robotic tasks. Electromobility is a six-month long autonomous driving competition organised by Continental Automotive in Iasi, Romania.

The challenge is to build an autonomous scale RC vehicle which has the ability to drive itself, recognise traffic signs and be controllable from a smartphone application. Practically this means hacking together an unpolished and unsafe version of a mini Tesla. In the qualification round we observed that the track surface was somewhat reflective, messing up the buggy line detector algorithm that I had programmed. Moreover, using the cheapest webcams available did not help at all.

Once findind out in the OBS Studio settings that Microsoft Lifecam webcams actually support manual exposure control, I have used v4l-ctl on Linux to adjust their corresponding parameters. This helped our three cameras distinguish something more than reflections.

Market segmentation example for airlines

We have decided to mount our three cameras in a V configuration on our chassis to offer maximum coverage of the track, especially on the sides and in front of the car. The cameras are tilted at around 30 degrees, and the complete image is combined after applying inverse perspective mapping in openCV for each of the cameras.

Since the competition track was considerably different from publicly available self driving car datasets, it quickly became more interesting to explore the latest techniques for generating training data. AirSim is an add-on for Unreal Engine which has many uses in computer vision tasks, deep learning and reinforcement learning. I really appreciate what the folks at Microsoft and their contributors have done by abstracting away all the intricate math required to generate RGBD, LiDAR and segmentation data.

All the code for the steering algorithm was developed in Python for portability. For curve fitting we have used weighted 2nd order polynomials. Steering commands are calculated from the lane angle and lateral offset using the formulas from this paper. Since the track dimensions were detailed on the competition page, I have reconstructed each separate interest class as a FBX 3D model using both Blender and Unreal Editor.

Track reflections were recreated by enhancing the road texture with normal maps. Once the map was built, I have included the AirSim addon in the project to be able to use the car vehicle and position the cameras on it. Training data was recorded from AirSim using the simGetImages API function and applying the first pipeline processing stage, since I needed the resulting images to contain perspective mapping and blending artifacts.

As so, a training pair would contain the following classes described below.

Microsoft Research Blog

The autoencoder network architecture is inspired by U-Netthe most significant difference being the reduced number of layers and filters for faster inference time on embedded hardware like the NVIDIA Jetson TX2. The advantages of applying a segmentation model are that you completely retain control the commands for steering, instead of relying on the network to do so, like in this the end-to-end AirSim cookbook example.

The primary disadvantage is that some form of algorithm needs to be implemented in order to filter the output of the network and ignore erroneous inputs. The simulator helped tremendously in developing and testing the complete solution. Around training examples were recorded in more than one hour of driving.

This will definitely run much faster if you use something more capable than a GTX based laptop. Some of the techniques used here to improve performance:. Once the final architecture was completed, training the model took approximately 44 minutes per one hundred epochs with batch sizes of 64 examples.

Training with a lot of disturbances was essential for our segmentation network to perform well in real life scenarios. Having a method of recreating these artifacts in the simulation proved really helpful for both dataset generation and end-to-end algorithm development and testing. This proved to be a very successful endeavor, since our team won the first prize in this competition.

The model was having a bad time, but I cannot blame it since I was barely able to distinguish the track lines as well.Typically we think that airlines will segment their customers by class of seating, such as economy class, business class and first class. In this market segmentation example for airlines, five distinct market segments are identified each having quite distinct needs and different evaluation and purchase approaches.

These five market segments as shown in the following diagram as well are:. These are non-business consumers that are frequent travelers via airlines.

airsim segmentation

Generally they would be older consumers, perhaps retirees, who have the time and money to holiday quite frequently. Because they are experienced travelers, they are likely to be loyal to a small number of airlines, depending upon their final destination. They would seek some comforts of travel and probably would not choose an airline simply based on price. They would be less likely to research airlines as well, as they are very experienced consumers in terms of airline travel. In fact, they are more likely to be opinion leaders, contributors to Trip Advisor and other similar sites.

Therefore, in addition to their frequency of purchase, they are an attractive and important market segment as they have significant ability to influence the purchase decisions of other consumers in the airline market. The second market segment of airline consumers are also quite regular airline travelers. Some may travel for business, but the majority will travel for personal reasons, such as holidays and visiting family. As suggested by their segment name, they are highly brand loyal to a particular airline wherever possible.

As a consequence of their brand loyalty, they may form an emotional view of the airlines brand that is, see them as a very good airlinefar less price sensitive and are far less willing to consider alternative airlines. This is an ideal target market for airlines, as they provide a long-term customer base. Of course, the difficulty with this market segment is attracting them in the first place they are far less willing to switch between airline brands.

Urgent travelers are infrequent users of airlines and generally represent a fairly small market segment in terms of size. These consumers have an urgent need to travel that is usually unexpected. Given their need to travel almost immediately, they are more concerned flight availability and destination requirements, rather than any consideration of price or airline brand. Airlines will typically withhold a handful of seats on each flight in the last few days prior to the flight to be sold at a premium price in expectation that a proportion of consumers will have an immediate need to travel.

Many businesses have operations in different parts of the country or will have sales opportunities in different cities, necessitating the need to frequently travel by plane. Generally, business customers make an organization-wide decision as to the choice of airline, rather than the individual traveler being involved in the purchase decision.May 29, By Microsoft blog editor.

By Ashish KapoorMicrosoft Research. Recent successes in machine learning ML and artificial intelligence AIwhich span from achieving human-level parity in speech recognition to beating world champions in board games, indicate the promise of the recent methods. Most of these successes, however, are limited to agents that live and operate in the closed world of software. Such closed-world operations provide two significant advantages to these AI agents. First, these agents need to excel only with respect to the one task they are designed for—an intelligent agent playing a board game needs only to reason about the next best move to make and nothing else.

Second, most of these systems enjoy the luxury to collect annotated, near-infinite training data, either from the tediously labeled past experience or via techniques such as self-play. Not only do these devices have to excel at their primary task, they also have to live in an open world with all kinds of unmodeled exogenous phenomenon and threats. Further, these systems need to adapt and learn with a minimal amount of training. Many of the recently successful paradigms, such as reinforcement learning, learning-by-demonstration, and transfer learning, are particularly challenging to apply on these devices given the need for a large amount of training data for these techniques.

While there have been examples of integrative AI, where an AI system might be realized via several individual components coming together, there is a need to explore the basic principles that might enable a core fabric to build adaptive and intelligent systems that work in the real world.

Using AirSim to develop autonomous driving algorithms

The inset shows depth, object segmentation and front camera streams generated in real time. At Microsoft Research, we are pursuing an ambitious agenda in the realm of robotics and cyberphysical systems, where the goal is to explore and reveal a unifying algorithmic and technological fabric that would enable such real-world artificial intelligence. Our belief is that there are three key aspects that need to be addressed at a fundamental level in order to take the next big leap in building AI agents for the real world.

These three aspects are structure, simulation, and safety, which we describe below:. Structure : One way to address the data scarcity issue is to use the structure, both statistical and logical, of the world. The order in the environment such as traffic rules, laws of nature, and our social circle can be very helpful in collapsing the uncertainty that an agent faces while operating in the real world.

For example, our recent work on No-Regret Replanning Under Uncertaintyshows how existing robotic path planning algorithms can exploit the statistical structure of winds in order to determine the near-optimal path to follow even when the data is scarce.

This figure shows the ability to generalize to different structured environments. The flying quadrotor, using the same underlying mechanism, learns to avoid obstacles autonomously for different environments. While the traditional approaches have encoded such relationships as a statistical or a logical model, the ability to truly operate in the wild world instead needs mechanisms to efficiently infer such relationships on their own.

Our recent work on Learning to Explore with Imitation is one big step in that direction, where the agent learns a policy while implicitly learning about the structure in the world. A key advantage of this approach is the fact that no explicit encoding about the structural knowledge is required, thereby allowing the algorithm to generalize across multiple problem domains. Simulation : Simulating the real world itself is an AI-complete task, but even an approximation of reality will serve as a fundamental building block in this ambitious quest.

Cameras and sensors

Our popular open-sourced simulation project aims to bridge such simulation-to-reality gaps. Not only are we using simulation to generate meaningful training data, but we also consider it an integral part of the AI agent as a portal to execute and verify all the actions they plan to take in the uncertain world. This is akin to how human beings might stop to think and simulate the consequences of their actions before acting in certain difficult situations.

The AI agents need the ability to be introspective and learn from the virtual thought process. Such execution traces of these plans or policies are instrumental for verifying the effectiveness and correctness of the planned trajectory. Key to success in this fundamental problem is the ability to transfer all the learnings and inferences that happen in simulation to the real world.

We continue to invest in and explore this exciting realm of sim-to-real AI. The architecture of the simulation system that depicts the core components and their interactions.

airsim segmentation

One possible cause of unsafe behavior is machine learning and perception systems that fail to collapse the uncertainty completely in the environment.

Similarly, these ideas are further extended to derive safe, bandit-based methods for decision making. There are many aspects of safety, including cybersecurity, verification, and testing, that we are exploring in collaboration with various colleagues. We show a hypothetical scenario where a robot needs to avoid an obstacle.

The imperfect sensing provides a system with a belief about the safe areas to travel blue and the red lines.To avoid this entirely, go to Project settings in Unreal Editor, go to Input tab and disable all settings for mouse capture. You can view the available settings options. For PX4 you can arm by holding both sticks on remote control down and to the center.

To fix this you can update the package like this:. But this might break something for example, PyTorch 0. To avoid this you should create new conda environment. To fix this make sure you build AirSim first run build. See Camera Views for information on the camera views and how to change them. Unreal 4. Note that all materials used on all LODs need to have the checkbox checked in order for dithered LOD transitions to work. When checked the transition of generated foliage will be a lot smoother and will look better than 4.

See XBox controller for details. See how to build a hexacopter. Here is multi-vehicle setup guide. It depends on how big your Unreal Environment is.

The Blocks environment that comes with AirSim is very basic and works on typical laptops. If you can also include logs, that could also expedite the investigation. File an issue through GitHub Issues. How do I arm my drone? Something went wrong. How do I debug? What do the colors mean in the Segmentation View?

Can I build a hexacopter with AirSim? How do I use AirSim with multiple vehicles? What computer do you need?

AIRSIM

How do I report issues? Where is the setting file and how do I modify it? Can I use an XBox controller to fly?PoseTracker uses deep learning to track the position and orientation of objects.

This solution will use your phone camera to measure and track the angle, orientation, and distance of an item in real time. Convolutional neural networks CNN has made significant strides in object recognition, classification, and segmentation, as used in self-driving vehicles, for example. PoseTracker is a collaborative proof of concept to solve 3D positioning. Convolutional neural networks, a class of deep neural network, has made significant strides in the recent years in terms of object recognition, classification and segmentation leading to significant development in self driving vehicles and a great variety of computer vision application.

However, there have been very few practical implementations of these advanced approaches in object 3D pose estimation. The ability to recognize and track the object in the 3D reference space is still a difficult problem to resolve due to some several challenging issues:.

The idea is to leverage the power of CNN and implement an application to recognize and track the pose position and orientation of objects in 3D with a patented optical marker that will help to identify the rotation and estimate the pose of the object. PoseTracker is a proof of concept for a simple object pose detection pipeline, integrated with rotation information based on a 3D pose tracking solution an optical marker.

The application analyzes the 2D images taken from a camera with the optical marker always visible. The application, with a supervised training, detects the marker, that infers its orientation information from one image to all subsequent images based on comparison to a predefined 3D orientation. This different approach to solve the pose tracker issues will help in the future, to use your phone camera get the angle, orientation, and distance that an object is from you in real time.

Clean Water AI uses deep learning to detect dangerous bacteria and particles in water. The device analyzes drinking water with real-time detection and contamination mapping. Caregivers can view a live stream from anywhere and receive notifications if the device detects any issues. Intelligent robotics uses AI to increase collaboration between people and devices. Microsoft AI enables the next generation of robots to adapt to dynamic situations and communicate naturally with people.

AirSim is a simulation tool that creates a 3D version of a real environment. AI uses the vision model to identify objects or people. Jumpstart your own AI innovations with learning resources and development solutions from Microsoft AI. Learn to create your own AI experiences with courses in AI technology.

Engage with learning paths in conversational AI, machine learning, AI for devices, cognitive services, autonomous systems, AI business strategies, and responsible AI. Start building AI solutions with powerful tools and services. PoseTracker PoseTracker uses deep learning to track the position and orientation of objects.


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