Localization Methods

There are several ways an FTC robot can determine its position on the field.

Most localization systems use some combination of drive encoders, odometry wheels, IMUs, and vision. FTC also has dedicated hardware that simplifies the process, such as the goBILDA Pinpoint and REV Robotics Expansion Hub / Control Hub encoder interfaces and the OctoQuad.

Each method has different advantages, limitations, and levels of complexity.

Drive Encoder Localization

The simplest approach is to use the encoders built into the drivetrain motors.

Since an encoder measures how far a motor has rotated, the robot can use the wheel's circumference and encoder measurements to estimate how far each wheel has traveled.

Encoder ticks
     ↓
Wheel rotation
     ↓
Distance traveled
     ↓
Robot movement
     ↓
Estimated pose

For example, a robot can use the difference between its left and right wheel movements to estimate how far it has driven and how much it has turned.

Advantages

• Requires no additional hardware
• Easy to set up
• Works well for simple autonomous routines
• Encoders are already built into many FTC motors

Disadvantages

Drive encoders measure the movement of the drive wheels, so their measurements can be affected by wheel slip.

This can become especially noticeable when:

• Strafing
• Turning
• Accelerating quickly
• Driving over inconsistent surfaces

Because errors accumulate over time, drive encoder localization is generally less reliable for highly precise autonomous routines.

Dead Wheel Odometry

Dead wheel odometry uses dedicated unpowered wheels to measure the robot's movement.

Unlike drivetrain wheels, dead wheels are designed specifically for tracking motion.

A common three-wheel setup contains:

       Forward
          ↑

     [ Parallel ]
          |
   [Perpendicular]

Two tracking wheels are parallel to each other, while another is perpendicular to them.

Encoders attached to these wheels measure how far each wheel rotates.

From these measurements, the robot can calculate:

• Forward/backward movement
• Sideways movement
• Rotation

Why Dead Wheels?

Because tracking wheels are not responsible for driving the robot, their measurements are less affected by drivetrain wheel slip.

This makes dead wheel odometry a popular choice for competitive FTC robots.

Two-Wheel vs. Three-Wheel

Two-wheel odometry uses two tracking wheels along with another source of heading information, usually an IMU.

Three-wheel odometry uses three tracking wheels to measure both translational movement and rotation.

Three-wheel odometry provides a complete measurement of planar motion from the tracking wheels, but requires additional hardware and careful mechanical alignment.

Advantages

• Consistent movement measurements
• Independent of drivetrain power
• Good for precise autonomous routines
• Works well with path-following systems

Disadvantages

• Requires additional hardware
• Tracking wheels must be mounted carefully
• Wheel diameter and encoder measurements must be calibrated
• Mechanical alignment affects accuracy

goBILDA Pinpoint

The goBILDA Pinpoint is an integrated odometry computer designed specifically for FTC robots.

Instead of having your Robot Controller perform all of the odometry calculations itself, Pinpoint handles the tracking-wheel measurements and calculates the robot's pose.

A typical Pinpoint setup uses two perpendicular odometry pods:

        Forward
           ↑

      ┌─────────┐
      │         │
      │   ROBOT │
      │         │
      └─────────┘
          │
     ─────┼─────
      Pinpoint
     odometry pods

The Pinpoint reads the encoder measurements from its odometry pods and uses them to calculate the robot's:

• X position
• Y position
• Heading

It also includes an onboard IMU for heading measurement.

Why Use Pinpoint?

Pinpoint simplifies the implementation of odometry.

Without a dedicated odometry computer, your software needs to:

1. Read encoder positions
2. Convert encoder ticks to distances
3. Account for the physical position of each tracking wheel
4. Calculate robot movement
5. Update the robot's pose

Pinpoint performs much of this work on the device itself and provides the resulting pose to the Robot Controller.

Advantages

• Designed specifically for FTC
• Integrated odometry calculations
• Built-in IMU
• Supports two-wheel odometry
• Reduces the amount of localization code needed
• Provides a consistent hardware interface

Disadvantages

• Requires dedicated hardware
• Still depends on proper odometry pod installation
• Requires calibration
• Software must communicate with the Pinpoint

Pinpoint is a particularly convenient option when a team wants dedicated odometry without implementing the complete localization system from scratch.

OctoQuad

The OctoQuad is an encoder interface that can read up to eight quadrature encoders.

Unlike the Pinpoint, the OctoQuad is not itself an odometry calculator.

Instead, it provides your Robot Controller with accurate encoder measurements that your localization software can use.

Odometry Wheels
      ↓
   Encoders
      ↓
   OctoQuad
      ↓
Encoder Positions
      ↓
Localization Software
      ↓
     Pose

This makes the OctoQuad useful for robots with multiple encoders that need to be read simultaneously.

For example, a three-wheel odometry system can connect its tracking-wheel encoders to an OctoQuad.

Your software then uses those encoder measurements to calculate the robot's movement and update its pose.

Advantages

• Supports up to eight quadrature encoders
• Centralizes encoder connections
• Reduces dependence on motor encoder ports
• Useful for custom odometry systems
• Can provide encoder data to your own localization code

Disadvantages

• Does not calculate robot pose by itself
• Requires localization software
• Requires additional odometry hardware if used for dead wheels
• Still requires calibration

Pinpoint vs. OctoQuad

The biggest distinction is where the localization calculation happens.

Pinpoint:

Odometry → Pinpoint → Pose


OctoQuad:

Odometry → OctoQuad → Encoder Data → Your Code → Pose

Pinpoint is an odometry computer, while OctoQuad is an encoder interface.

The OctoQuad therefore gives teams more control over how they implement localization, while Pinpoint handles more of the underlying odometry calculation for you.

IMU Localization

An IMU, or Inertial Measurement Unit, measures the robot's motion using inertial sensors.

For FTC localization, the most useful measurement is usually heading.

IMU
 ↓
Heading
 ↓
Robot orientation

An IMU can provide an independent measurement of the robot's orientation instead of relying entirely on wheel movement.

Encoder + IMU

An IMU is often combined with encoder-based localization rather than being used by itself.

For example:

Encoders → X/Y movement
IMU      → Heading
             ↓
        Robot Pose

The encoders estimate how the robot moved, while the IMU provides heading information.

This is particularly common with two-wheel odometry.

Limitations

IMUs are not perfect.

Measurements can be affected by:

• Sensor noise
• Calibration
• Magnetic interference on certain sensor types
• Integration drift

An IMU is therefore usually most useful as one part of a larger localization system.

Vision-Based Localization

Vision can also be used to determine the robot's position.

Instead of measuring how far the robot has moved, a camera can observe known objects on the field and calculate the robot's pose relative to them.

One common example is AprilTags.

Camera
   ↓
Detect AprilTag
   ↓
Determine tag position
   ↓
Calculate robot pose

If the robot knows the location of an AprilTag on the field, the camera can use the tag's observed position and orientation to estimate where the robot is.

Advantages

• Can provide an absolute reference
• Does not accumulate wheel-based error in the same way as odometry
• Useful for correcting accumulated localization drift
• Can provide both position and heading

Disadvantages

• Requires a camera
• Performance depends on lighting and camera placement
• Tags can be temporarily blocked
• Detection introduces processing time
• Pose accuracy depends on camera calibration

Vision is often most useful when combined with odometry rather than replacing it completely.

Sensor Fusion

The most robust localization systems combine multiple sensors.

This is called sensor fusion.

For example, a robot might combine:

Dead Wheels ──┐
              ├──→ Localization ──→ Pose
IMU ──────────┤
              │
AprilTags ────┘

Each sensor provides different information.

By combining these measurements, a robot can take advantage of the strengths of each sensor.

For example, odometry can provide continuous position updates while the robot is moving, and an AprilTag detection can provide an absolute reference that helps correct accumulated error.

Comparing Localization Methods

These categories are not absolute. A well-calibrated simple system can outperform a poorly calibrated advanced one.

Mechanical consistency and calibration are just as important as the localization hardware.

Choosing a Localization System

For a beginner robot, drive encoders may be enough.

For a competitive robot, dead wheel odometry is a common choice.

If you want dedicated odometry with less localization code, goBILDA Pinpoint is a convenient option.

If you want direct access to multiple encoder measurements and control over the localization algorithm, REV Robotics OctoQuad is a powerful option.

For advanced systems, odometry can be combined with an IMU and vision to provide both continuous tracking and absolute position corrections.

A useful progression is:

Drive Encoders
      ↓
Dead Wheel Odometry
      ↓
Pinpoint / OctoQuad
      ↓
Odometry + IMU
      ↓
Odometry + IMU + Vision

You do not need the most complicated system to have good localization. Choose the simplest system that provides the accuracy your robot needs.

What's Next?

Once your robot can estimate its pose, you can use that information to control where it goes.

Continue to Path Following to learn how autonomous systems use localization to follow paths and correct their movement.