TY - GEN
T1 - Direct visual SLAM fusing proprioception for a humanoid robot
AU - Scona, Raluca
AU - Nobili, Simona
AU - Petillot, Yvan R.
AU - Fallon, Maurice
PY - 2017/12/14
Y1 - 2017/12/14
N2 - In this paper we investigate the application of semi-dense visual Simultaneous Localisation and Mapping (SLAM) to the humanoid robotics domain. Challenges of visual SLAM applied to humanoids include the type of dynamic motion executed by the robot, a lack of features in man-made environments and the presence of dynamics in the scene. Previous research on humanoid SLAM focused mostly on feature-based methods which result in sparse environment reconstructions. Instead, we investigate the application of a modern direct method to obtain a semi-dense visually interpretable map which can be used for collision free motion planning. We tackle the challenge of using direct visual SLAM on a humanoid by proposing a more robust pose tracking method. This is formulated as an optimisation problem over a cost function which combines information from the stereo camera and a low-drift kinematic-inertial motion prior. Extensive experimental demonstrations characterise the performance of our method using the NASA Valkyrie humanoid robot in a laboratory environment equipped with a Vicon motion capture system. Our experiments demonstrate pose tracking robustness to challenges such as sudden view change, motion blur in the image, change in illumination and tracking through sequences of featureless areas in the environment. Finally, we provide a qualitative evaluation of our stereo reconstruction against a LIDAR map.
AB - In this paper we investigate the application of semi-dense visual Simultaneous Localisation and Mapping (SLAM) to the humanoid robotics domain. Challenges of visual SLAM applied to humanoids include the type of dynamic motion executed by the robot, a lack of features in man-made environments and the presence of dynamics in the scene. Previous research on humanoid SLAM focused mostly on feature-based methods which result in sparse environment reconstructions. Instead, we investigate the application of a modern direct method to obtain a semi-dense visually interpretable map which can be used for collision free motion planning. We tackle the challenge of using direct visual SLAM on a humanoid by proposing a more robust pose tracking method. This is formulated as an optimisation problem over a cost function which combines information from the stereo camera and a low-drift kinematic-inertial motion prior. Extensive experimental demonstrations characterise the performance of our method using the NASA Valkyrie humanoid robot in a laboratory environment equipped with a Vicon motion capture system. Our experiments demonstrate pose tracking robustness to challenges such as sudden view change, motion blur in the image, change in illumination and tracking through sequences of featureless areas in the environment. Finally, we provide a qualitative evaluation of our stereo reconstruction against a LIDAR map.
UR - http://www.scopus.com/inward/record.url?scp=85041947730&partnerID=8YFLogxK
U2 - 10.1109/IROS.2017.8205943
DO - 10.1109/IROS.2017.8205943
M3 - Conference contribution
AN - SCOPUS:85041947730
T3 - IEEE/RSJ International Conference on Intelligent Robots and Systems
SP - 1419
EP - 1426
BT - 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PB - IEEE
T2 - 30th IEEE/RSJ International Conference on Intelligent Robots and Systems 2017
Y2 - 24 September 2017 through 28 September 2017
ER -