Visual Odometry Algorithm

Visual odometry (VO) algorithms estimate a vehicle's position and movement using visual input, primarily from cameras, aiming for robust and accurate pose estimation. Current research focuses on improving VO's resilience to challenging conditions like low-texture environments, adverse weather (e.g., rain), and complex robot motions by incorporating sensor fusion (e.g., LiDAR, IMU, wheel odometry), neural network-based kinematic model learning, and geometric constraints (e.g., Manhattan world assumption). These advancements are crucial for reliable navigation in autonomous vehicles, robotics, and other applications requiring precise localization in dynamic or unstructured settings.