Document Type

Conference Proceeding

Publication Date



When an autonomous vehicle operates in an unknown environment, it must remember the locations of environmental objects and use those object to maintain an accurate location of itself. This vehicle is faced with Simultaneous Localization and Mapping (SLAM), a circularly defined robotics problem of map building with no prior knowledge. The SLAM problem is a difficult but critical component of autonomous vehicle exploration with applications to search and rescue missions. This paper presents the first SLAM solution combining stereo cameras, inertial measurements, and vehicle odometry into a Multiple Integrated Navigation Sensor (MINS) path. The FastSLAM algorithm, modified to make use of the MINS path, observes and maps the environment with a LIDAR unit. The MINS FastSLAM algorithm closes a 140 meter loop with a path error that remains within 1 meter of surveyed truth. This path reduces the error 79% from an odometry FastSLAM output and uses 30% of the particles.


U.S. Government work not protected by U.S. copyright.

AFIT Scholar furnishes the accepted version of this conference paper. The published version of record is available from IEEE via subscription at the DOI link on this record page.

Source Publication

IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, 2011, pp. 859-864.