Beyond Point Clouds: Fisher Information Field for Active Visual Localization

For mobile robots to localize robustly, actively considering the perception requirement at the planning stage is essential. In this paper, we propose a novel representation for active visual localization. By formulating the Fisher information and sensor visibility carefully, we are able to summarize the localization information into a discrete grid, namely the Fisher information field. The information for arbitrary poses can then be computed from the field in constant time, without the need of costly iterating all the 3D landmarks. Experimental results on simulated and real-world data show the great potential of our method in efficient active localization and perception-aware planning. To benefit related research, we release our implementation of the information field to the public.

[1]  Jens Wawerla,et al.  Feature-rich path planning for robust navigation of MAVs with Mono-SLAM , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Davide Scaramuzza,et al.  Perception-aware Receding Horizon Navigation for MAVs , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[3]  F. Pukelsheim.  Optimal Design of Experiments , 1993 .

[4]  Davide Scaramuzza,et al.  PAMPC: Perception-Aware Model Predictive Control for Quadrotors , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[5]  Luca Carlone,et al.  Attention and Anticipation in Fast Visual-Inertial Navigation , 2019, IEEE Transactions on Robotics.

[6]  B. Efron,et al.  Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information , 1978 .

[7]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[8]  Wolfram Burgard,et al.  Active Mobile Robot Localization , 1997, IJCAI.

[9]  Matthias Nießner,et al.  Real-time 3D reconstruction at scale using voxel hashing , 2013, ACM Trans. Graph..

[10]  Horst Bischof,et al.  Active monocular localization: Towards autonomous monocular exploration for multirotor MAVs , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Alexei Makarenko,et al.  An experiment in integrated exploration , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Roland Siegwart,et al.  Path planning for motion dependent state estimation on micro aerial vehicles , 2013, 2013 IEEE International Conference on Robotics and Automation.

[13]  Hauke Strasdat,et al.  Local accuracy and global consistency for efficient SLAM , 2012 .

[14]  M. J. Box.  Bias in Nonlinear Estimation , 1971 .

[15]  Vijay Kumar,et al.  Trajectory Optimization On Manifolds with Applications to SO(3) and R3XS2 , 2018, Robotics: Science and Systems.

[16]  Roland Siegwart,et al.  Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization , 2013, Robotics: Science and Systems.

[17]  Paolo Valigi,et al.  Perception-aware Path Planning , 2016, ArXiv.

[18]  Marco Pavone,et al.  Robust Motion Planning via Perception-Aware Multiobjective Search on GPUs , 2017, ArXiv.

[19]  David W. Murray,et al.  Simultaneous Localization and Map-Building Using Active Vision , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Frank Dellaert,et al.  Planning in the continuous domain: A generalized belief space approach for autonomous navigation in unknown environments , 2015, Int. J. Robotics Res..

[21]  Roland Siegwart,et al.  Voxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MAV planning , 2016, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[22]  Christos Papachristos,et al.  Uncertainty-aware receding horizon exploration and mapping using aerial robots , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[23]  W MurrayDavid,et al.  Simultaneous Localization and Map-Building Using Active Vision , 2002 .

[24]  Michael Gassner,et al.  SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems , 2017, IEEE Transactions on Robotics.

[25]  Vadim Indelman,et al.  Active online visual-inertial navigation and sensor calibration via belief space planning and factor graph based incremental smoothing , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[26]  Wolfram Burgard,et al.  Coastal navigation-mobile robot navigation with uncertainty in dynamic environments , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[27]  John J. Leonard,et al.  Adaptive Mobile Robot Navigation and Mapping , 1999, Int. J. Robotics Res..

[28]  R. Bajcsy.  Active perception , 1988 .