Robots Learn to Navigate Spaces Where Landmarks Move
Researchers have solved a fundamental problem in autonomous vehicle navigation: how robots can reliably map and locate themselves in environments where physical objects shift position. The breakthrough matters for mining operations, warehouses, and any setting requiring long-term robot autonomy—where static mapping assumptions fail in the real world.
Originaltitel: Multi-Hypothesis SLAM for Non-Static Environments with Reoccurring Landmarks
<p>A static world assumption is often used when considering the <em>simultaneous localization and mapping</em> (SLAM) problem. In reality, especially when long-term autonomy is the objective, this is not valid. This paper studies a scenario where uniquely identifiable landmarks can attend multiple discrete positions, not known a priori . Based on a feature based multi-hypothesis map representation, a multi-hypothesis SLAM algorithm is developed inspired by target tracking theory. The creation of such a map is merged into the SLAM framework allowing any available SLAM method to solve the underlying mapping and localization problem for each hypothesis. A recursively updated hypothesis score allows for hypothesis rejection and prevents exponential growth in the number of hypotheses. The developed method is evaluated in an underground mine application, where physical barriers can be moved in between multiple distinct positions. Simulations are conducted in this environment showing the benefits of the multi-hypothesis approach compared to executing a standard SLAM algorithm. Practical considerations as well as suitable approximations are elaborated upon and experiments on real data further validates the simulated results and show that the multi-hypothesis approach has similar performance in reality as in simulation.</p>