This paper presents a novel method to reduce the scale drift for indoor monocular simultaneous localization and mapping (SLAM). We leverage the prior knowledge that in the indoor environment, the line segments form tight clusters, e.g. many door frames in a straight corridor are of the same shape, size and orientation, so the same edges of these door frames form a tight line segment cluster. We implement our method in the popular ORB-SLAM2, which also serves as our baseline. In the front end we detect the line segments in each frame and incrementally cluster them in the 3D space. In the back end, we optimize the map imposing the constraint that the line segments of the same cluster should be the same. Experimental results show that our proposed method successfully reduces the scale drift for indoor monocular SLAM.
@cite_27: This paper proposes a novel solution to the problem of scale drift in single-camera simultaneous localization and mapping, based on recognizing and measuring objects. When reconstructing the trajectory of a camera moving in an unknown environment, the scale of the environment, and equivalently the speed of the camera, is obtained by accumulating relative scale estimates over sequences of frames. This leads to scale drift: errors in scale accumulate over time. The proposed solution is to learn the classes of objects that appear throughout the environment and to use measurements of the size of these objects to improve the scale estimate. A bag-of-words-based scheme to learn object classes, to recognize object instances, and to use these observations to correct scale drift is described and is demonstrated reducing accumulated errors by 64 while navigating for 2.5 km through a dynamic outdoor environment.
@cite_20: In this paper we propose a novel Semantic Bundle Adjustment framework whereby known rigid stationary objects are detected while tracking the camera and mapping the environment. The system builds on established tracking and mapping techniques to exploit incremental 3D reconstruction in order to validate hypotheses on the presence and pose of sought objects. Then, detected objects are explicitly taken into account for a global semantic optimization of both camera and object poses. Thus, unlike all systems proposed so far, our approach allows for solving jointly the detection and SLAM problems, so as to achieve object detection together with improved SLAM accuracy.
@cite_12: Navigation in unknown environments requires the robot to obtain the destination positions without a map. The utilization of model-based object recognition would be a solution, where the robot can estimate the destination positions from geometric relationships between the recognized objects and the robot. This paper presents a robot system for this kind of navigation, in which the robot navigates itself to the room designated by room number. The robot has an environment model including a corridor and a door with a room number plate, and utilizes the model for the efficient recognition of the objects and the estimation of their positions.
@cite_26: We present a real-time object-based SLAM system that leverages the largest object database to date. Our approach comprises two main components: (1) a monocular SLAM algorithm that exploits object rigidity constraints to improve the map and find its real scale, and (2) a novel object recognition algorithm based on bags of binary words, which provides live detections with a database of 500 3D objects. The two components work together and benefit each other: the SLAM algorithm accumulates information from the observations of the objects, anchors object features to especial map landmarks and sets constrains on the optimization. At the same time, objects partially or fully located within the map are used as a prior to guide the recognition algorithm, achieving higher recall. We evaluate our proposal on five real environments showing improvements on the accuracy of the map and efficiency with respect to other state-of-the-art techniques. Complete monocular Visual SLAM system that creates maps of points and 3D objects.Novel SLAM back-end formulation optimizes map points, object poses and map scale.Novel object detection algorithm manages large-scale object databases in real time.SLAM and object detection work together to enhance mapping and object recognition.Accuracy results outperform other state-of-the-art RGB and RGB-D SLAM approaches.
Some attempts have been made to integrate object recognition results into the SLAM system @cite_12 @cite_20 @cite_27 @cite_26 , so the scale information can be obtained from known objects. These methods require training a detector or classifier ahead, and assume that particular objects can be encountered by the robot, which does not usually occur in a real application scenario. Our method does not need pre-training, nor access to a model database during running. The only assumption made in our algorithm is that the line segments in an indoor environment are quantized and form tight clusters, which is usually true in a modern building.