Let's review: SLAM is Simultaneous Localization and Mapping, a very traditional algorithm for autonomous robot navigation that allows the robot to keep track of its own location while, at the same time, building a map of its environment. Robots using SLAM frequently rely on information about their own kinematics, and rangefinding sensors such as laser or sonar. RatSLAM appeared in 2004 in the paper RatSLAM: a hippocampal model for simultaneous localization and mapping (PDF format). It presented a new, biomemetic approach to SLAM inspired by the design of the rodent hippocampus, which evolved to handle the same type of navigation tasks. A C++ implementation of RatSLAM is available as free software under the GNU GPL. So what's new? BatSLAM:
"We propose to combine a biomimetic navigation model which solves a simultaneous localization and mapping task with a biomimetic sonar mounted on a mobile robot to address two related questions. First, can robotic sonar sensing lead to intelligent interactions with complex environments? Second, can we model sonar based spatial orientation and the construction of spatial maps by bats?"
These questions are answered in a new paper by Jan Steckel and Herbert Peremans titled, BatSLAM: Simultaneous Localization and Mapping Using Biomimetic Sonar. According to the authors, when you combine the best of rat brain and bat brain algorithms, the "biomimetic navigation model operating on the information from the biomimetic sonar allows an autonomous agent to map unmodified (office) environments efficiently and consistently". Personally, I think after the robot successfully navigates a new environment using this algorithm, it should roll up to you and say, "I'm BatSLAM!"