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

Keyframe-based monocular SLAM: design, survey, and future directions

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery

DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments

Simultaneous Localization and Mapping (SLAM) is considered to be a fundamental capability for intelligent mobile robots. Over the past decades, many impressed SLAM systems have been developed and achieved good performance under certain circumstances. However, some problems are still not well solved, for example, how to tackle the moving objects in the dynamic environments, how to make the robots truly understand the surroundings and accomplish advanced tasks. In this paper, a robust semantic visual SLAM towards dynamic environments named DS-SLAM is proposed. Five threads run in parallel in DS-SLAM: tracking, semantic segmentation, local mapping, loop closing, and dense semantic map creation. DS-SLAM combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in the real-world environment. The results demonstrate the absolute trajectory accuracy in DS-SLAM can be improved by one order of magnitude compared with ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic environments. Now the code is available at our github: https://github.com/ivipsourcecode/DS-SLAMComment: 7 pages, accepted at the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018). Now the code is available at our github: https://github.com/ivipsourcecode/DS-SLA

ORB-SLAM: a Versatile and Accurate Monocular SLAM System

This paper presents ORB-SLAM, a feature-based monocular SLAM system that operates in real time, in small and large, indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves unprecedented performance with respect to other state-of-the-art monocular SLAM approaches. For the benefit of the community, we make the source code public.Comment: 17 pages. 13 figures. IEEE Transactions on Robotics, 2015. Project webpage (videos, code): http://webdiis.unizar.es/~raulmur/orbslam

Benchmarking and Comparing Popular Visual SLAM Algorithms

This paper contains the performance analysis and benchmarking of two popular visual SLAM Algorithms: RGBD-SLAM and RTABMap. The dataset used for the analysis is the TUM RGBD Dataset from the Computer Vision Group at TUM. The dataset selected has a large set of image sequences from a Microsoft Kinect RGB-D sensor with highly accurate and time-synchronized ground truth poses from a motion capture system. The test sequences selected depict a variety of problems and camera motions faced by Simultaneous Localization and Mapping (SLAM) algorithms for the purpose of testing the robustness of the algorithms in different situations. The evaluation metrics used for the comparison are Absolute Trajectory Error (ATE) and Relative Pose Error (RPE). The analysis involves comparing the Root Mean Square Error (RMSE) of the two metrics and the processing time for each algorithm. This paper serves as an important aid in the selection of SLAM algorithm for different scenes and camera motions. The analysis helps to realize the limitations of both SLAM methods. This paper also points out some underlying flaws in the used evaluation metrics.Comment: 7 pages, 4 figure