CSAIL Research Abstracts - 2005 link to http://publications.csail.mit.edu/abstracts/abstracts05/index.html link to http://www.csail.mit.edu
bullet Introduction bullet Architecture, Systems
& Networks
bullet Language, Learning,
Vision & Graphics
bullet Physical, Biological
& Social Systems
bullet Theory bullet

horizontal line

Active Exploration for Maximising Map Accuracy

Nicholas Roy


Tremendous progress has been made recently in simultaneous localisation and mapping of unknown environments. Using sensor and odometry data from an exploring mobile robot, it has become much easier to build high-quality globally consistent maps of many large, real-world environments. However, relatively little attention has been given to the trajectories that generate the data for building these maps. Exploration strategies are often used to cover the largest amount of unknown space as quickly as possible, but few strategies exist for building the most reliable map possible. Nevertheless, the particular control strategy can have a substantial impact on the quality of the resulting map. We have developed control algorithms for exploring unknown space that explicitly tries to build as large a map as possible while maintaining as accurate a map as possible.


Recording the sensor data for a good map is not necessarily a straight-forward process, even done manually. The control strategy used to acquire the data can have a substantial impact on the quality of the resulting map; different kinds of motions can lead to greater or smaller errors in the mapping process. Ideally, we would like to automate the exploration process, not just for efficient exploration of unknown areas of space, but exploration that will lead to high accuracy maps. How We consider the problem of building an accurate map in the Extended Kalman Filter framework [1]. The approach we take is to use a one-step lookahead, choosing trajectories that maximise the space explored while minimising the likelihood we will become lost on re-entering known space. In this case, the single step is over a path from the existing map through unexplored space to the first sensor reading from within the known map. At every time step, we will choose a trajectory that will minimise our uncertainty as we re-enter the map, at the same time maximising the coverage of the unexplored area. We use a parameterised class of paths and repeatedly choose the parameter setting that maximises our objective function. The class of paths is given by the parametric curve [2]:

\end{displaymath} (1)

which expresses the distance $r$ of the robot from the origin as a function of time; $k$ is a dilating constant and $n$ parameterises the curve to control the frequency with which the robot moves toward the origin. The rate of new space covered as a function of $\theta$ decreases roughly monotonically as $n$ increases, since for larger $n$ the robot spends an increasing amount of time in previously explored territory.

For a particular trajectory $r(\cdot, n)$, we define an objective function for computing the optimal trajectory, $q(n)$, from $t_0$ to $t_f$ as the amount of unexplored space covered from $t_0$ to $t_f$, reduced by the uncertainty of state estimate at time $t_f$. Let us use $U(r(t_i, n))$ as an indicator function, whether or not the pose of the robot at time $t_i$ is in explored space. There are many choices for quantifying the uncertainty of the EKF filter at time $t_f$: we will use one of the most common functions, the determinant of the covariance matrix, $\vert Cov(t_f)\vert$. These two functions give us our objective function

q(n ; t_0) = \sum_{t_i = t_0}^{t_f} U(r(n, t_i))^2 - \lambda \vert Cov(t_f) \vert.
\end{displaymath} (2)

This function contains the one free parameter $\lambda$ that allows us to vary how aggressive the exploration of unknown space should be compared with building a high-accuracy map.


Given the policy class $p_n(t)$ and objective function $r(p_n(t))$ described above, we want to find $n$ to maximise $r(p_n(\cdot))$. For a particular state of the Extended Kalman Filter at time $t_0$, we cannot compute the covariance at time $t_f$ in closed form so we use numerical simulation, projecting the Kalman filter forward until the trajectory enters unexplored space and then returns into the map. We discretise $n$ and evaluate $q(n_i)$ for each choice of $n$.

Figure: Two example policies, that represent different trade-offs between map certainty and exploration rate. The ground truth trajectory of the robot ('o' curve) is plotted against the inferred map ('+' curve). The mean error of the map generated from the trajectory the left is 4.6cm, comparable with the most conservative policies yet with a significantly higher exploration rate.

Figure: A comparison against some heuristic policies. Notice that the online policy is comparable in map accuracy to the best heuristic, and also comparable in exploration efficiency (as determined by path length) to the best heuristic for that metric.

The online strategy currently contains at some open problems. Firstly, the reward function contains an explicit trade-off between exploration and map accuracy, represented by the free parameter $\lambda$. Ideally, the optimal control representation would not contain any free parameters. We may be able to eliminate this free parameter by choosing a different reward function, but this is a question for further research. Secondly, the particular parameterisation may not be the best policy class. This policy class is somewhat restrictive, in that the single parameter essentially represents a frequency of returning to the origin. We may be able to achieve better results using a more general policy class, such as one of the stochastic policy classes in the reinforcement learning literature. Finally, the strategy is greedy, in that it attempts to choose the trajectory based on a single projection into the future. However, as more of the map is acquired, it may become possible to infer unseen map structure and make more intelligent decisions, leading to even better performance in more structured and regular environments.


This is joint work with Robert Sim and Gregory Dudek.


[1]J. J. Leonard, I. J. Cox, and H. F. Durrant-Whyte. Dynamic map building for an autonomous mobile robot. Int. J. Robotics Research, 11(4):286--298, 1992.

[2] Robert Sim and Gregory Dudek. Effective exploration strategies for the construction of visual maps. In Proc. IJCAI Workshop on Reasoning with Uncertainty in Robotics (RUR), pages 69--76, 2003.

[3] Robert Sim, Gregory Dudek, and Nicholas Roy. Online control policy optimization for minimizing map uncertainty during exploration. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2004), New Orleans, LA, April 2004.

horizontal line

MIT logo Computer Science and Artificial Intelligence Laboratory (CSAIL)
The Stata Center, Building 32 - 32 Vassar Street - Cambridge, MA 02139 - USA
tel:+1-617-253-0073 - publications@csail.mit.edu
(Note: On July 1, 2003, the AI Lab and LCS merged to form CSAIL.)