When performing tasks in unknown environments it is useful for a team of robots to have a good map of the area to assist in efficient, collision-free planning and navigation. A recent paper by members of the DCIST alliance tackles the problem of autonomous mapping of unknown environments using information theoretic metrics and signed distance field maps. Signed distance fields are discrete representations of environmental occupancy in which each cell of the environment stores a distance to the nearest obstacle surface, with negative distances indicating that the cell is within an obstacle. Such a representation has many benefits over the more traditional occupancy grid map including trivial collision checking, and easy extraction of mesh representations of the obstacle surfaces. The researchers use a truncated signed distance field, which only keeps track of cells near obstacle surfaces, and model each cell as a Gaussian random variable with an expected distance and a variance determined incrementally using a realistic RGB-D sensor noise model. The use of Gaussian random variables enables the closed form computation of Shannon mutual information between a Gaussian sensor measurement and the Gaussian cells it intersects. This allows for efficient evaluations of expected information when planning and evaluating possible future trajectories. Using these tools, a robot is able to efficiently evaluate a large number of trajectories before choosing the best next step to increase its information about the environment. The researchers show the resulting active exploration algorithm running on several simulated 2D environments of varying complexity. The figure shows a snapshot of the robot exploring the most complex of the three environments. These simulations can be viewed in more detail in the video linked below.
Points of Contact: Vijay Kumar (PI), Kelsey Saulnier.
Citation: K. Saulnier., N. Atanasov, G. J.Pappas, & V. Kumar, “Information Theoretic Active Exploration in Signed Distance Fields,” IEEE International Conference on Robotics and Automation (ICRA), Paris, France, June 2020. (Accepted)