TY - JOUR
T1 - Stereo-Based Visual Odometry for Autonomous Robot Navigation
AU - Kostavelis, Ioannis
AU - Boukas, Evangelos
AU - Nalpantidis, Lazaros
AU - Gasteratos, Antonios
PY - 2016/2/10
Y1 - 2016/2/10
N2 - Mobile robots should possess accurate self-localization capabilities in order to be successfully deployed in their environment. A solution to this challenge may be derived from visual odometry (VO), which is responsible for estimating the robot's pose by analysing a sequence of images. The present paper proposes an accurate, computationally-efficient VO algorithm relying solely on stereo vision images as inputs. The contribution of this work is twofold. Firstly, it suggests a non-iterative outlier detection technique capable of efficiently discarding the outliers of matched features. Secondly, it introduces a hierarchical motion estimation approach that produces refinements to the global position and orientation for each successive step. Moreover, for each subordinate module of the proposed VO algorithm, custom non-iterative solutions have been adopted. The accuracy of the proposed system has been evaluated and compared with competent VO methods along DGPS-assessed benchmark routes. Experimental results of relevance to rough terrain routes, including both simulated and real outdoors data, exhibit remarkable accuracy, with positioning errors lower than 2%.
AB - Mobile robots should possess accurate self-localization capabilities in order to be successfully deployed in their environment. A solution to this challenge may be derived from visual odometry (VO), which is responsible for estimating the robot's pose by analysing a sequence of images. The present paper proposes an accurate, computationally-efficient VO algorithm relying solely on stereo vision images as inputs. The contribution of this work is twofold. Firstly, it suggests a non-iterative outlier detection technique capable of efficiently discarding the outliers of matched features. Secondly, it introduces a hierarchical motion estimation approach that produces refinements to the global position and orientation for each successive step. Moreover, for each subordinate module of the proposed VO algorithm, custom non-iterative solutions have been adopted. The accuracy of the proposed system has been evaluated and compared with competent VO methods along DGPS-assessed benchmark routes. Experimental results of relevance to rough terrain routes, including both simulated and real outdoors data, exhibit remarkable accuracy, with positioning errors lower than 2%.
KW - Incremental Motion Estimation
KW - Mobile Robots
KW - Outlier Detection
KW - Visual Odometry
KW - Visual Odometry
KW - Outlier Detection
KW - Incremental Motion Estimation
KW - Mobile Robots
UR - http://www.scopus.com/inward/record.url?scp=85002292081&partnerID=8YFLogxK
U2 - 10.5772/62099
DO - 10.5772/62099
M3 - Journal article
AN - SCOPUS:85002292081
SN - 1729-8806
VL - 13
JO - International Journal of Advanced Robotic Systems
JF - International Journal of Advanced Robotic Systems
IS - 1
M1 - 21
ER -