Because a SLAM map is three-dimensional, however, it does a better job of distinguishing objects that are near each other than single-perspective analysis can. The system devised by Pillai and Leonard, a professor of mechanical and ocean engineering, uses the SLAM map to guide the segmentation of images captured by its camera before feeding them to the object-recognition algorithm. It thus wastes less time on spurious hypotheses.
More important, the SLAM data let the system correlate the segmentation of images captured from different perspectives. Analyzing image segments that likely depict the same objects from different angles improves the system’s performance.
Using machine learning, other researchers have built object-recognition systems that act directly on detailed 3-D SLAM maps built from data captured by cameras, such as the Microsoft Kinect, that also make depth measurements. But unlike those systems, Pillai and Leonard’s system can exploit the vast body of research on object recognizers trained on single-perspective images captured by standard cameras.
Moreover, the performance of Pillai and Leonard’s system is already comparable to that of the systems that use depth information. And it’s much more reliable outdoors, where depth sensors like the Kinect’s, which depend on infrared light, are virtually useless.
Pillai and Leonard’s new paper describes how SLAM can help improve object detection, but in ongoing work, Pillai is investigating whether object detection can similarly aid SLAM. One of the central challenges in SLAM is what roboticists call “loop closure.” As a robot builds a map of its environment, it may find itself somewhere it’s already been – entering a room, say, from a different door. The robot needs to be able to recognize previously visited locations, so that it can fuse mapping data acquired from different perspectives.
Object recognition could help with that problem. If a robot enters a room to find a conference table with a laptop, a coffee mug, and a notebook at one end of it, it could infer that it’s the same conference room where it previously identified a laptop, a coffee mug, and a notebook in close proximity.
“The ability to detect objects is extremely important for robots that should perform useful tasks in everyday environments,” says Dieter Fox, a professor of computer science and engineering at the University of Washington. “This work shows very promising results on how a robot can combine information observed from multiple viewpoints to achieve efficient and robust detection of objects.”