Visual-Inertial Odometry for Autonomous Robots

Visual-Inertial Odometry for Autonomous Robots A robot needs to estimate how it moves through the world. GPS is unavailable indoors, wheel odometry can slip, and LiDAR may not always be available. Visual-Inertial Odometry (VIO) combines cameras and IMUs to estimate motion. Basic Idea Camera ---> Visual Features ---+ | v State Estimator ^ | IMU ----> Motion Information ---+ | v Robot Trajectory The camera provides visual constraints. The IMU provides high-frequency motion measurements. Why Combine Them? A camera gives rich spatial information but can suffer from: Motion blur Low texture Poor lighting Slow frame rate An IMU operates at much higher rates but accumulates drift when integrated over time. Their weaknesses are complementary. Feature Tracking A simple visual pipeline might be: Image | v Feature Detection | v Feature Tracking | v Motion Estimation Feature types may include corners or learned visual features. IMU Prediction The IMU can predict how the robot's state changes between camera frames. Conceptually: Previous State | +--> IMU measurements | v Predicted State | +--> Camera observation | v Corrected State This is the prediction/correction pattern used by many estimators. Initialization VIO initialization is important because the estimator must determine quantities such as: Initial orientation Gravity direction Velocity Scale for monocular systems Sensor biases Poor initialization can cause instability later. ROS 2 Architecture /camera/image | v Visual Frontend ----+ | /imu/data ----------+--> VIO Estimator --> /odometry | +--> /tf Use consistent timestamps and calibrated camera-IMU extrinsics. Improving Robustness Useful techniques include: Rejecting outlier feature matches Monitoring IMU saturation Handling dropped frames Estimating sensor biases Detecting low-texture scenes Monitoring estimator health Evaluation Evaluate against a trusted trajectory where available. Useful metrics include: Absolute trajectory error Relative pose error Drift per dista