Integrating the CREVE framework with complementary sensors like cameras or LiDARs can significantly enhance the robustness and accuracy of localization solutions. One effective approach is to employ a multi-sensor fusion strategy that combines the strengths of each sensor modality. For instance, while the mmWave radar provides reliable velocity measurements and performs well in adverse weather conditions, cameras can offer rich visual information for feature extraction and object recognition.
A practical implementation could involve using a Kalman filter or a pose graph optimization framework that fuses data from the radar, IMU, and visual sensors. The radar can provide ego-velocity estimates, while the camera can contribute to position estimates through visual odometry techniques. The LiDAR can complement this by offering precise distance measurements and 3D mapping capabilities, which can be particularly useful in complex environments.
Moreover, the CREVE framework can be adapted to include visual-inertial odometry (VIO) techniques, where the visual data is used to correct and refine the radar-based ego-velocity estimates. This can be achieved by incorporating visual features into the optimization process, allowing the system to leverage both the spatial and temporal information from the visual and radar data.
Additionally, implementing a robust outlier rejection mechanism that considers the data from all sensors can further enhance the reliability of the localization solution. By cross-validating the measurements from different sensors, the system can effectively filter out erroneous data and improve overall estimation accuracy. This multi-sensor approach not only enhances the robustness of the CREVE framework but also broadens its applicability across various robotic platforms and environments.