- while it is interesting to see the grounding of the proposed method in neuroscience, some of the general ideas are already present in other methods for exploration, in particular, reasoning topologically is captured by methods that use the generalized Voronoi graph or semantic maps to guide the exploration, and the long-term storage through pose graphs in SLAM, where loop closure is applied (discussed in graph-based slam appendix section), or curiosity-driven exploration. The paper should discuss the proposed method with respect to such methods.
- the paper's comparison is limited in considering only the standard frontier-based exploration, when in fact there are a number of exploration methods showing better performance than the standard one, both in terms of exploration, as well as planning time. Some examples both classic and learning based include:
Cao, C., Zhu, H., Choset, H., & Zhang, J. (2021, July). TARE: A Hierarchical Framework for Efficiently Exploring Complex 3D Environments. In Robotics: Science and Systems (Vol. 5).
Lindqvist, B., Agha-Mohammadi, A. A., & Nikolakopoulos, G. (2021, September). Exploration-RRT: A multi-objective path planning and exploration framework for unknown and unstructured environments. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3429-3435). IEEE.
Shrestha, R., Tian, F. P., Feng, W., Tan, P., & Vaughan, R. (2019, May). Learned map prediction for enhanced mobile robot exploration. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 1197-1204). IEEE.
Caley, J. A., Lawrance, N. R., & Hollinger, G. A. (2019). Deep learning of structured environments for robot search. Autonomous Robots, 43, 1695-1714.
- the gain in memory appears to be a major component of the proposed method, however, overall, the trend seems to be fairly close to the frontier-based approach and somewhat surprising given the use of local maps. In fact, for the realistic experiments, in AWS office, memory appears better for Frontier. The size of each local map might depend on the complexity of the environment, but it is worth discussing what affects the determination of the local map in practice.
A couple of minor presentation comments:
- to be more precise in assumptions and corresponding presentation of functions, it is worth mentioning that the robot is non-omnidirectional, as otherwise the indicator function for whether the frontier edge is spatially behind the agent wouldn't apply. In addition, for that function there would be a threshold to determine what "behind" means, with respect to the orientation of the robot.
- usually white pixels are used for free space, instead of black.
- "FRAGMENTAION" -> "FRAGMENTATION"
- "that work did not seriously explore" -> "that work did not explore in-depth"
- instead of calling "wall-clock time" it is better to characterize it with "planning time"
- while it is interesting to see the grounding of the proposed method in neuroscience, some of the general ideas are already present in other methods for exploration, in particular, reasoning topologically is captured by methods that use the generalized Voronoi graph or semantic maps to guide the exploration, and the long-term storage through pose graphs in SLAM, where loop closure is applied (discussed in graph-based slam appendix section), or curiosity-driven exploration. The paper should discuss the proposed method with respect to such methods.