To further enhance the state estimation performance of the OptiState framework, various exteroceptive sensors beyond depth cameras can be integrated into the learning-based component. These sensors can provide complementary information about the robot's environment and improve the robustness of the state estimation system. Some examples of exteroceptive sensors that can be integrated include:
Lidar Sensors: Lidar sensors can provide detailed 3D point cloud data of the robot's surroundings, enabling accurate mapping and localization in complex environments. By incorporating lidar data into the learning-based component, the framework can improve its perception capabilities and enhance state estimation accuracy.
RGB Cameras: RGB cameras can capture color images of the environment, which can be used for object detection, scene understanding, and visual odometry. By processing RGB images with convolutional neural networks (CNNs) or other computer vision algorithms, the framework can extract valuable features for state estimation and improve its performance in visually challenging scenarios.
Inertial Measurement Units (IMUs): IMUs can provide information about the robot's acceleration, angular velocity, and orientation. By fusing IMU data with other sensor modalities, such as joint encoders and vision data, the framework can enhance its estimation of dynamic states and improve its resilience to external disturbances.
By integrating a diverse set of exteroceptive sensors and leveraging their complementary information, the OptiState framework can achieve more robust and accurate state estimation for legged robots in various operating conditions.