Learning to Prevent Monocular SLAM Failure using Reinforcement Learning

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.Comment: Accepted at the 11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP) 2018 More info can be found at the project page at https://robotics.iiit.ac.in/people/vignesh.prasad/SLAMSafePlanner.html and the supplementary video can be found at https://www.youtube.com/watch?v=420QmM_Z8v

Adapting Monte Carlo Localization to Utilize Floor and Wall Texture Data

Monte Carlo Localization (MCL) is an algorithm that allows a robot to determine its location when provided a map of its surroundings. Particles, consisting of a location and an orientation, represent possible positions where the robot could be on the map. The probability of the robot being at each particle is calculated based on sensor input. Traditionally, MCL only utilizes the position of objects for localization. This thesis explores using wall and floor surface textures to help the algorithm determine locations more accurately. Wall textures are captured by using a laser range finder to detect patterns in the surface. Floor textures are determined by using an inertial measurement unit (IMU) to capture acceleration vectors which represent the roughness of the floor. Captured texture data is classified by an artificial neural network and used in probability calculations. The best variations of Texture MCL improved accuracy by 19.1\% and 25.1\% when all particles and the top fifty particles respectively were used to calculate the robot\u27s estimated position. All implementations achieved comparable performance speeds when run in real-time on-board a robot

Tactile Mapping and Localization from High-Resolution Tactile Imprints

This work studies the problem of shape reconstruction and object localization using a vision-based tactile sensor, GelSlim. The main contributions are the recovery of local shapes from contact, an approach to reconstruct the tactile shape of objects from tactile imprints, and an accurate method for object localization of previously reconstructed objects. The algorithms can be applied to a large variety of 3D objects and provide accurate tactile feedback for in-hand manipulation. Results show that by exploiting the dense tactile information we can reconstruct the shape of objects with high accuracy and do on-line object identification and localization, opening the door to reactive manipulation guided by tactile sensing. We provide videos and supplemental information in the project's website http://web.mit.edu/mcube/research/tactile_localization.html.Comment: ICRA 2019, 7 pages, 7 figures. Website: http://web.mit.edu/mcube/research/tactile_localization.html Video: https://youtu.be/uMkspjmDbq