The navigating task to the given coordinates initializes the agent at a random place on the map. The goal is the
target coordinates, which are set as ("Go 5 m to the north, 3 m to the west relative to the beginning"). The room
map is not available to the agent, and during the evaluation process, the agent can only use the input from the
RGB-D camera for navigation.
The agent had four actions: forward, turn left, turn right, and stop. Evaluation occurs when the agent selects the 'STOP' action. As a metric, SPL (Success weighted by Path Length) is used. The episode is considered successful if, when calling 'STOP,' the agent is within 0.36 m (2x radius of the agent) from the coordinates of the target.
As the overall approach, we used DF-VO as a localization module and trained DDPPO with its coordinates. This approach gave us SPL around 0.32 in normal condtions and 0.16 in noisy conditions. We evaluated these results at ten different maps and took the average. The first experiment (zero pos) is to pass zero coordinates to DDPPO, the second experiment (ground truth pos) is to pass ideal coordinates from the environment to the DDPPO. For the RTAB-MAP, turn angle was reduced from 10 degrees to 5, since RTAB-MAP cannot track position with such a big difference between frames. With the presence of sensor noise, RTAB-MAP also fails, and output zero position at every step, but in good conditions outperform DF-VO by far. The main reason why both RTAB-MAP and DF-VO performance significantly worst than ground truth coordinates, it is hard to determine the final stopping place. Especially if the goal is near the wall, an agent could reconstruct the goal coordinates on the other side of the wall due to the localization error.
|Zero pos||Ground truth pos||RTAB-MAP pos||DF-VO pos|
|Action and sensor noise||0.08||0.58||0.09||0.16|
Extensive work has been done to study and test modules for the RL agent in the Habitat environment. We trained the bunch of state of the art solutions for navigation and building maps on the premises and developed a solution for the point goal task. Focusing on the article DF-VO and DDPPO, we built a combination of these algorithms for the realistic noise conditions in the new Habitat environment. DF-VO was used to determine the position of the agent, which allowed us not to use ground truth coordinates from the environment, relying only on the RGB-D sensor. DDPPO was used to control the agent, relying on the reconstructed coordinates, and showed an excellent ability to adapt to all noises and imperfections of the environment. In the future, we plan to improve the overall performance and transfer the policy from a simulator to a real-world robot.