Towards benchmarks for vision SLAM algorithms

Abstract

SLAM in an outdoor environment using natural landmarks stands as the holy grail of SLAM algorithms. Segmenting landmarks from background clutter in such environments is difficult and vision, rather than laser, has a higher potential to perform such tasks due to the higher bandwidth of information it carries. There is a need to establish a benchmark upon which emerging vision SLAM algorithms can be assessed and compared. Towards this objective, this paper proposes the infrastructure for such a benchmark and discusses the issues involved in compiling it. Ego-motion information is extracted via a strap-down inertial measurement unit (IMU). Synchronized Global Positioning System (GPS), IMU, and surrounding images of an outdoor park environment are compiled into a database. IMU data in tested on an inertial navigation system (INS) dead-reckoning algorithm. The adequacy of the stereo image database is validated by extracting disparity maps of each stereo image in the database. IMU simulations show the necessity for visual SLAM to improve pose estimation. The complete data set, including GPS, IMU, and stereo images is available for downloading purposes