The figure above shows the progress made with the implementation of the base navigation stack on the Locobot (wheeled platform). This visualization was completed through handheld mapping which involved setting up the RTAB-Map on the lab computer and building it from the source. After installing the RTAB-Map, the SLAM algorithm was integrated with ROS (by base software developers) so interfacing with the Realsense viewer and visualization software did not cause any issues. After the implementation, the handheld mapping was tested by manipulating the realsense camera directly and a partial map of the environment (MRSD Lab) was visualized.
During the initial few weeks, we implemented the RTAB-Map system; we later transitioned to the Kimera SLAM system. All the associated modules, including Kimera-VIO, KimeraRPGO, and Kimera-Semantics, were successfully integrated. The initial testing phase employed the uHumans dataset to evaluate the performance and compatibility of the integrated modules.
Subsequently, the RealSense D435i sensor was incorporated into the system and further tests were conducted in a variety of environments, such as the NSH basement, the A-level floor, and the MRSD Lab as shown in Figure 11. These diverse testing environments helped to ensure the robustness and adaptability of the Kimera SLAM system under different conditions.
In addition to the core Kimera modules, a MobileNet based semantic segmentation model was implemented and fused with Kimera-Semantics to enhance the system’s understanding of the environment. This process involved utilizing a pre-trained semantic segmentation model for the implementation of the module. To ensure optimal performance, the model was optimized using TensorRT, ultimately achieving a frame rate of 15 fps.
By integrating the Kimera SLAM system and implementing the semantic segmentation algorithm, the SLAM subsystem has been significantly enhanced, enabling it to more accurately map and localize the robot within its environment. During the Fall semester, further extensive testing, optimization, and integration will be carried out to ensure seamless compatibility with the planning stack. Additionally, a more suitable semantic segmentation model will be selected to better adapt to our specific environment.