Vision enhanced navigation for unmanned systems

Brandon Loy Wampler, Purdue University

Abstract

A vision based simultaneous localization and mapping (SLAM) algorithm is evaluated for use on unmanned systems. SLAM is a technique used by a vehicle to build a map of an environment while concurrently keeping track of its location within the map, without a priori knowledge. The work in this thesis is focused on using SLAM as a navigation solution when global positioning system (GPS) service is degraded or temporarily unavailable. Previous work on unmanned systems that lead up to the determination that a better navigation solution than GPS alone is first presented. This previous work includes control of unmanned systems, simulation, and unmanned vehicle hardware testing. The proposed SLAM algorithm follows the work originally developed by Davidson et al. in which they dub their algorithm MonoSLAM [1–4]. A new approach using the Pyramidal Lucas-Kanade feature tracking algorithm from Intel's OpenCV (open computer vision) library is presented as a means of keeping correct landmark correspondences as the vehicle moves through the scene. Though this landmark tracking method is unusable for long term SLAM due to its inability to recognize revisited landmarks, as opposed to the Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), its computational efficiency makes it a good candidate for short term navigation between GPS position updates. Additional sensor information is then considered by fusing INS and GPS information into the SLAM filter. The SLAM system, in its vision only and vision/IMU form, is tested on a table top, in an open room, and finally in an outdoor environment. For the outdoor environment, a form of the slam algorithm that fuses vision, IMU, and GPS information is tested. The proposed SLAM algorithm, and its several forms, are implemented in C++ using an Extended Kalman Filter (EKF). Experiments utilizing a live video feed from a webcam are performed. The different forms of the filter are compared and conclusions are made on the effectiveness of the SLAM algorithm for use on the current unmanned vehicles in Purdue's Hybrid Systems Lab. Recommendations for future improvements in applying computer vision to unmanned systems are also presented in the final chapter.

Degree

M.S.A.A.

Advisors

Hwang, Purdue University.

Subject Area

Aerospace engineering|Robotics|Computer science

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