Linear RGB-D SLAM for Planar Environments

Pose estimation and map reconstruction are basic requirements for robotic autonomous behavior. In this paper, we propose a point–plane-based method to simultaneously estimate the robot’s poses and reconstruct the current environment’s map using RGB-D cameras. First, we detect and track the point and plane features from color and depth images, and reliable constraints are obtained, even for low-texture scenes. Then, we construct cost functions from these features, and we utilize the plane’s minimal representation to minimize these functions for pose estimation and local map optimization. Furthermore, we extract the Manhattan World (MW) axes on the basis of the plane normals and vanishing directions of parallel lines for the MW scenes, and we add the MW constraint to the point–plane-based cost functions for more accurate pose estimation. The results of experiments on public RGB-D datasets demonstrate the robustness and accuracy of the proposed algorithm for pose estimation and map reconstruction, and we show its advantages compared with alternative methods.

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

RGB-D SLAM Using Point–Plane Constraints for Indoor Environments

Guo

Peng

Wang

et al. 2019

“…ORB-SLAM2 [7] is the state-of-the-art feature-point based visual SLAM system and it has a RGB-D implementation. L-SLAM [25] is a RGB-D SLAM system using planes and MW constraints. Note that we test ORB-SLAM2 using the open-source code provided by the author and we include the results of L-SLAM from [25] directly.…”

Section: Methodsmentioning

confidence: 99%

“…Zhou et al [24] utilize mean-shift to track dominant directions of MW and achieve drift-free rotation by decoupling the estimation of rotation and translation. Some other works [25,26,27] also exploit planes of MW to estimate drift-free rotation. These algorithms work well in some specific scenes, but they are also easy to fail because the MW assumption is not valid for some scenes.…”

Section: Related Workmentioning

confidence: 99%

Point-Plane SLAM Using Supposed Planes for Indoor Environments

Zhang

Wang

et al. 2019

Simultaneous localization and mapping (SLAM) is a fundamental problem for various applications. For indoor environments, planes are predominant features that are less affected by measurement noise. In this paper, we propose a novel point-plane SLAM system using RGB-D cameras. First, we extract feature points from RGB images and planes from depth images. Then plane correspondences in the global map can be found using their contours. Considering the limited size of real planes, we exploit constraints of plane edges. In general, a plane edge is an intersecting line of two perpendicular planes. Therefore, instead of line-based constraints, we calculate and generate supposed perpendicular planes from edge lines, resulting in more plane observations and constraints to reduce estimation errors. To exploit the orthogonal structure in indoor environments, we also add structural (parallel or perpendicular) constraints of planes. Finally, we construct a factor graph using all of these features. The cost functions are minimized to estimate camera poses and global map. We test our proposed system on public RGB-D benchmarks, demonstrating its robust and accurate pose estimation results, compared with other state-of-the-art SLAM systems.

“…Out of all these shapes, the major ones naturally create “obstacles” or define “edges” beyond which the vehicles cannot protrude, and these planes can be extracted and employed as features or landmarks in SLAM applications. Indeed, due to the geometric simplicity of the plane shapes and their abundance in human-inhabited environments, planar features have attracted increasing attention from both the computer graphics and robotics community in recent years [16,17,18]. As for the smaller plane shapes, they may be from the profiles of small objects or even moving objects such as vehicles.…”

Section: Introductionmentioning

confidence: 99%

State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds

Gostar

Chuah

et al. 2019

There is a large body of literature on solving the SLAM problem for various autonomous vehicle applications. A substantial part of the solutions is formulated based on using statistical (mainly Bayesian) filters such as Kalman filter and its extended version. In such solutions, the measurements are commonly some point features or detections collected by the sensor(s) on board the autonomous vehicle. With the increasing utilization of scanners with common autonomous cars, and availability of 3D point clouds in real-time and at fast rates, it is now possible to use more sophisticated features extracted from the point clouds for filtering. This paper presents the idea of using planar features with multi-object Bayesian filters for SLAM. With Bayesian filters, the first step is prediction, where the object states are propagated to the next time based on a stochastic transition model. We first present how such a transition model can be developed, and then propose a solution for state prediction. In the simulation studies, using a dataset of measurements acquired from real vehicle sensors, we apply the proposed model to predict the next planar features and vehicle states. The results show reasonable accuracy and efficiency for statistical filtering-based SLAM applications.