SLAM-Safe Planner: Preventing Monocular SLAM Failure using Reinforcement
Learning
release_3klkykjbdjejxccc4gb6mjti6a
by
Vignesh Prasad, Saurabh Singh, Nahas Pareekutty, Balaraman Ravindran,
Madhava Krishna
2016
Abstract
Effective SLAM using a single monocular camera is highly preferred due to its
simplicity. However, when compared to trajectory planning methods using
depth-based SLAM, Monocular SLAM in loop does need additional considerations.
One main reason being that for the optimization, in the form of Bundle
Adjustment (BA), to be robust, the SLAM system needs to scan the area for a
reasonable duration. Most monocular SLAM systems do not tolerate large camera
rotations between successive views and tend to breakdown. Other reasons for
Monocular SLAM failure include ambiguities in decomposition of the Essential
Matrix, feature-sparse scenes and more layers of non linear optimization apart
from BA. This paper presents a novel formulation based on Reinforcement
Learning (RL) that generates fail safe trajectories wherein the SLAM generated
outputs (scene structure and camera motion) do not deviate largely from their
true values. Quintessentially, the RL framework successfully learns the
otherwise complex relation between motor actions and perceptual inputs that
result in trajectories that do not cause failure of SLAM, which are almost
intractable to capture in an obvious mathematical formulation. We show
systematically in simulations how the quality of the SLAM map and trajectory
dramatically improves when trajectories are computed by using RL.
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