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 # fuse
 The fuse stack provides a general architecture for performing sensor fusion live on a robot. Some possible applications
 include state estimation, localization, mapping, and calibration.
+
+## Overview
+fuse is a ROS framework for performing sensor fusion using nonlinear least squares optimization techniques. In
+particular, fuse provides:
+* a plugin-based system for modeling sensor measurements
+* a similar plugin-based system for motion models
+* a plugin-based system for publishing optimized state values
+* an extensible state variable definition
+* a "contract" on how an optimizer will interact with the above components
+* and some common implementations to get everyone started
+
+(unpresented) ROSCon 2018 Lightning Talk [slides](doc/fuse_lightning_talk.pdf)
+
+
+Data flows through the system approximately like this:
+* A sensor model receives raw sensor data. The sensor model generates a constraint and sends it to the optimizer.
+* The optimizer receives the new sensor constraint. A request is sent to each configured motion model to generate
+  a constraint between the previous state and the new state involved in the sensor constraint.
+* The motion model receives the request and generates the required constraints to connect the new state to the
+  previously generated motion model chain. The motion model constraints are sent to the optimizer.
+* The optimizer adds the new sensor model and motion model constraints and variables to the graph and
+  computes the optimal values for each state variable.
+* The optimal state values are sent to each configured publisher (as well as the sensor models and motion models).
+* The publishers receive the optimized state values and publish any derived quantities on ROS topics.
+* Repeat
+
+It is important to note that much of this flow happens asynchronously in practice. Sensors are expected to operate
+independently from each other, so each sensor will be sending constraints to the optimizer at its own frequency. The
+optimizer will cache the constraints and process them in small batches on some schedule. The publishers may
+require considerable processing time, introducing a delay between the completion of the optimization cycle and the
+publishing of data to the ROS topic.
+
+![fuse sequence diagram](doc/fuse_sequence_diagram.png)
+
+## Example
+Let's consider a simple robotics example to illustrate this. Assume we have a typical indoor differential-drive robot.
+This robot has wheel encoders and a horizontal laser.
+
+The first thing we must do is define our state variables. At a minimum, we want the robot pose at each timestamp.
+We model the pose using a 2D position and an orientation. Each 2D pose _at a specific time_ gets a unique variable
+name. For ease of notation, let's call the pose variables `X1`, `X2`, `X3`, etc. (In reality, each variable gets
+a UUID, but those are much harder to write down.) Each 2D pose is instantiated as a `example_robot::Pose2D` which is
+derived from the `fuse_core::Variable` base class. (`fuse` ships with several basic variables, such as 2D and 3D
+versions of position, orientation, and velocity variables, but you can derive your own variable types as you need them.)
+
+Next we need to decide how to model our sensors. We can model the wheel encoders as providing an incremental
+pose measurement. Given a starting pose, `X1`, and a wheel encoder delta, `z`, we predict the pose `X2'` using some
+measurement function `f`.
+
+`X2' = f(X1, z)`
+
+The error term for our constraint is the difference between the predicted pose `X2'` and the actual pose `X2`
+
+`error = X2'^-1 * X2`
+
+where `X2'^-1` is the inverse of the pose `X2'`
+
+We derive a `fuse_core::Constraint` that implements that error function. Similarly, we perform scan-to-scan matching
+using out laser data and create an incremental pose constraint between consecutive scans.
+
+In the simplest example, the sensors are synchronized, i.e. the laser and the wheel encoders are sampled at the same
+time. This is enough to construct our first `fuse` system. Below is the constraint graph generated from this first
+system. The large circles represent state variables at a given time, while the small squares represent measurements.
+The graph connectivity indicates which variables are involved in what measurements.
+
+![fuse graph](doc/fuse_graph_1.png)
+
+The two sensor models are configured as plugins to an optimizer implementation. The optimizer performs the required
+computation to generate the optimal state variable values based on the provided sensor constraints. We will never be
+able to exactly satisfy both the wheel encoder constraints and the laserscan constraints. Instead we minimize the error
+of all the constraints using nonlinear least squares optimization.
+
+![fuse optimizer](doc/fuse_optimizer_1.png)
+
+While our `fuse` system is optimizing constraints from two different sensors, it is not yet publishing any data back
+out to ROS. In order to publish data to ROS, we derive a `fuse_core::Publisher` class and add it to the
+optimizer. Derived publishers have access to the optimized values of all state variables. The specific publisher
+implementation determines what type of messages are published and at what frequency. For our example system,
+we would like visualize the current pose of the robot in RViz, so we create a `fuse` publisher that finds the most
+recent pose and converts it into a `geometry_msgs::PoseStamped` message, then publishes the message to a topic.
+
+![fuse optimizer](doc/fuse_optimizer_2.png)
+
+We finally have something that is starting to be useful.
+
+### Adaptation #1: Asynchronous sensors
+
+Typically the laser measurements and the wheel encoder measurements are not synchronized. The encoder measurements are
+sampled faster than the laser, and are sampled at different times using a different clock. If we do not do anything
+different in this situation, the constraint graph becomes disconnected.
+
+![fuse graph](doc/fuse_graph_2.png)
+
+This is where motion models come into play. A motion model differs from a sensor model in that constraints can be
+generated between any two requested timestamps. Motion model constraints are generated upon request, not due to their
+own internal clock. We use the motion model to connect the states introduced by the other sensor measurements. We
+derive a class from the `fuse_core::MotionModel` base class and implement a differential drive kinematic
+constraint for our robot.
+
+![fuse optimizer](doc/fuse_optimizer_3.png)
+
+The motion models are also configured as plugins to the optimizer. The optimizer requests motion models constraints
+from the configured plugins whenever new states are created by the sensor models.
+
+![fuse graph](doc/fuse_graph_3.png)
+
+### Adaptation #2: Full path publishing
+
+Nothing about the `fuse` framework limits you to having a single publisher. What if you want to visualize the entire
+robot trajectory, instead of just the most recent pose? Well, we can create a new derived `fuse_core::Publisher` class
+that publishes all of the robot poses using a `nav_msgs::Path` message.
+
+![fuse optimizer](doc/fuse_optimizer_4.png)
+
+### Adaptation #3: Changing kinematics
+
+In your spare time, you also build [autonomous power wheels racers](http://www.powerracingseries.org/). But race cars
+don't use differential drive; you need a different motion model. Easy enough. We simply derive a new
+`fuse_core::MotionModel` class that implements an Ackermann steering model. Everything else can be reused.
+
+![fuse optimizer](doc/fuse_optimizer_5.png)
+
+![fuse graph](doc/fuse_graph_4.png)
+
+### Adaptation #4: Online calibration
+
+Over time you notice that the accuracy of the odometry measurements is decreasing. After some investigation you realize
+that the soft rubber racing tires are wearing, decreasing the diameter of the wheels over time. It sure would be nice
+if the odometry system could compensate for that automatically. To do that, we derive a new variable type from
+`fuse_core::Variable` that holds a single scalar value representing a wheel diameter at a specific point in time. For
+ease of notation, we refer to this new variable as `D1, D2, ...`, etc. We also need to derive a new wheel encoder sensor
+model from the `fuse_core::SensorModel` base class. This new sensor model involves the previous pose and next pose as
+before, but it also involves the previous wheel diameter. Finally, we need a `fuse_core::MotionModel` that describes
+how the wheel diameter is expected to change over time. Maybe some sort of exponential decay? And for good measure, we
+derive a new publisher plugin from `fuse_core::Publisher` that publishes the current wheel diameter. This allows us to
+plot how the wheel diameter changes over the length of the race.
+
+![fuse optimizer](doc/fuse_optimizer_6.png)
+
+![fuse graph](doc/fuse_graph_5.png)
+
+Now our system estimates the wheel diameters at each time step as well as the robot's pose.
+
+## The Math
+Internally `fuse` uses Google's [Ceres Solver](http://ceres-solver.org) to perform the nonlinear least squares
+optimization, which produces the optimal state variable values. I direct any interested parties to the Ceres Solver
+["Non-linear Least Squares"](http://ceres-solver.org/nnls_tutorial.html) tutorial for an excellent primer on the core
+concepts and involved math.
+
+## Summary
+The purpose of `fuse` is to provide a framework for performing sensor fusion tasks, allowing common components to be
+reused between systems, while also allowing components to be customized for different use cases. The goal is to allow
+end users to concentrate on modelling the robot, sensor, system, etc. and spend less time wiring the different
+sensor models together into runable code. And since all of the models are implemented as plugins, separate plugin
+libraries can be shared or kept private at the discretion of their authors.
+
+## API
+Class API details coming soon...
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