Visual-inertial Simultaneous Localization Research Articles

Indoor fixed-point hovering control for UAVs based on visual inertial SLAM

Purpose Autonomous flight of unmanned aerial vehicles (UAVs) in global position system (GPS)-denied environments has become an increasing research hotspot. This paper aims to realize the indoor fixed-point hovering control and autonomous flight for UAVs based on visual inertial simultaneous localization and mapping (SLAM) and sensor fusion algorithm based on extended Kalman filter. Design/methodology/approach The fundamental of the proposed method is using visual inertial SLAM to estimate the position information of the UAV and position-speed double-loop controller to control the UAV. The motion and observation models of the UAV and the fusion algorithm are given. Finally, experiments are performed to test the proposed algorithms. Findings A position-speed double-loop controller is proposed, by fusing the position information obtained by visual inertial SLAM with the data of airborne sensors. The experiment results of the indoor fixed-points hovering show that UAV flight control can be realized based on visual inertial SLAM in the absence of GPS. Originality/value A position-speed double-loop controller for UAV is designed and tested, which provides a more stable position estimation and enabled UAV to fly autonomously and hover in GPS-denied environment.

Evaluating SLAM Performance with Synthesized Datasets from Unreal-Based Emulators

The rapid advancement in visual-inertial simultaneous localization and mapping (SLAM) has opened numerous applications in computer vision. However, the scarcity of high quality, publicly accessible datasets hampers the evaluation of SLAM performance in varied and tailored environments. In this study, I employed the AirSim simulator and the Unreal Engine 4 to generate a trajectory resembling that of the TUM VI Room 1 ground truth dataset within the ArchViz indoor environment representing a well-lit, furnished room. I further modified the environment and trajectory through various expansions, addition of features, and data smoothing to ensure a more stable sequence of input frames into the SLAM architecture. I then examined the efficiency of visual ORB-SLAM3 by inputting images of resolution 256×144 and 512×288 at 30 frames per second (FPS), while also adjusting the feature threshold - the maximum number of feature points that ORB-SLAM3 tracks per frame. This investigation of the camera parameters within AirSim and ORB-SLAM3 has led to the essential finding that the resolution of the input images must coincide with the dimensions of the film. The subsequent runs under these variables reveal that higher resolution images lead to considerably better tracking, with an optimal feature threshold ranging between 3000~12000 feature points per frame. Moreover, ORB- SLAM3 demonstrated significantly enhanced robustness within dynamic environments containing moving objects when using higher resolution inputs, with a decreased error of close to 0cm compared to 23.19cm for lower resolutions (averaged over three runs). Finally, I conducted qualitative testing using real-life indoor environments recorded with an iPhone Xr camera, which produces results that highlight the challenges faced by ORB-SLAM3 due to factors such as glare and motion blur.

Stereo Visual Inertial Pose Estimation Based on Feedforward and Feedbacks

In this article, we present a stereo visual inertial pose estimation method based on feedforward and feedbacks. Compared to the widely used filter-based or optimization-based approaches, the proposed method only stores the most recent pose and measurements and thus can achieve fast processing. A gradient decreased feedback, a roll-pitch feedforward, and a bias estimation feedback, are introduced to fuse the vision and the inertial measurements. This system, which is called feedforward and feedback based visual inertial system (FVIS), is evaluated on the popular European robotics challenge micro aerial vehicle (EuRoC MAV) dataset. FVIS achieves high accuracy and robustness with respect to existing visual inertial simultaneous localization and mapping (SLAM) approaches. FVIS has also been implemented and tested on a unmanned aerial vehicle (UAV) platform. The source code developed during this study is available publicly.

Validation of real-time inside-out tracking and depth realization technologies for augmented reality-based neuronavigation.

Concomitant with the significant advances in computing technology, the utilization of augmented reality-based navigation in clinical applications is being actively researched. In this light, we developed novel object tracking and depth realization technologies to apply augmented reality-based neuronavigation to brain surgery. We developed real-time inside-out tracking based on visual inertial odometry and a visual inertial simultaneous localization and mapping algorithm. The cube quick response marker and depth data obtained from light detection and ranging sensors are used for continuous tracking. For depth realization, order-independent transparency, clipping, and annotation and measurement functions were developed. In this study, the augmented reality model of a brain tumor patient was applied to its life-size three-dimensional (3D) printed model. Using real-time inside-out tracking, we confirmed that the augmented reality model remained consistent with the 3D printed patient model without flutter, regardless of the movement of the visualization device. The coordination accuracy during real-time inside-out tracking was also validated. The average movement error of the X and Y axes was 0.34 ± 0.21 and 0.04 ± 0.08mm, respectively. Further, the application of order-independent transparency with multilayer alpha blending and filtered alpha compositing improved the perception of overlapping internal brain structures. Clipping, and annotation and measurement functions were also developed to aid depth perception and worked perfectly during real-time coordination. We named this system METAMEDIP navigation. The results validate the efficacy of the real-time inside-out tracking and depth realization technology. With these novel technologies developed for continuous tracking and depth perception in augmented reality environments, we are able to overcome the critical obstacles in the development of clinically applicable augmented reality neuronavigation.

Visual–Inertial Navigation System Based on Virtual Inertial Sensors

In this paper, we propose a novel visual–inertial simultaneous localization and mapping (SLAM) method for intelligent navigation systems that aims to overcome the challenges posed by dynamic or large-scale outdoor environments. Our approach constructs a visual–inertial navigation system by utilizing virtual inertial sensor components that are mapped to the torso IMU under different gait patterns through gait classification. We apply a zero-velocity update (ZUPT) to initialize the system with the original visual–inertial information. The pose information is then iteratively updated through nonlinear least squares optimization, incorporating additional constraints from the ZUPT to improve the accuracy of the system’s positioning and mapping capabilities in degenerate environments. Finally, the corrected pose information is fed into the solution. We evaluate the performance of our proposed SLAM method in three typical environments, demonstrating its applicability and high precision across various scenarios. Our method represents a significant advancement in the field of intelligent navigation systems and offers a promising solution to the challenges posed by degenerate environments.

LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base

In order to accurately locate personnel in underground spaces, positioning equipment is required to be mounted on wearable equipment. But the wearable inertial personnel positioning equipment moves with personnel and the phenomenon of measurement reference wobble (referred to as moving base) is bound to occur, which leads to inertial measurement errors and makes the positioning accuracy degraded. A neural network-assisted binocular visual-inertial personnel positioning method is proposed to address this problem. Using visual-inertial Simultaneous Localization and Mapping to generate ground truth information (including position, velocity, acceleration data, and gyroscope data), a trained neural network is used to regress 6-dimensional inertial measurement data from the IMU data fragment under the moving base, and a position loss function is constructed based on the regressed inertial data to reduce the inertial measurement error. Finally, using vision as the observation quantity, the point feature and inertial measurement data are tightly coupled to optimize the mechanism to improve the personnel positioning accuracy. Through the actual scene experiment, it is verified that the proposed method can improve the positioning accuracy of personnel. The positioning error of the proposed algorithm is 0.50%D, and it is reduced by 92.20% under the moving base.

Multi-Sensor Fusion-Based Indoor Single-Track Semantic Map Construction and Localization

The advancement of smartphones has been a prerequisite for multi-sensor fusion-based indoor mapping due to their high penetration and low cost. This article constructs a sparse single-track semantic map by fusing waypoints, semantic landmarks, and Wi-Fi landmarks, which are scene-representable and navigational in unknown indoor environments. The pedestrian dead reckoning (PDR)-aided visual-inertial simultaneous localization and mapping (PDR-aided VI-SLAM) method uses the PDR velocity as an external observation to constrain the pre-integration of inertial measurements. The semantic objects detected by the YOLO V4 are pre-filtered by the proposed semantic object filtering algorithm before semantics matching. The centroid of feature points clustered by the random-sampling spatial clustering algorithm (R-DBSCAN) characterizes the location of a semantic landmark relative to the waypoints estimated by the PDR-aided VI-SLAM. To enhance the stability of Wi-Fi landmarks, Wi-Fi signals are processed by the proposed sliding window-based Wi-Fi fusion algorithm. By using waypoints as an intermediate quantity, the association relationships between waypoints, semantic landmarks, and Wi-Fi landmarks are established. After that, a lightweight single-track semantic map is constructed. A landmark matching-based localization method is proposed to evaluate the similarity between the local map and the constructed venue map to infer the location of a pedestrian in the venue map. Experiments are conducted in the office building and mall scenes under various illumination conditions and semantic density. Results demonstrate the high quality of the single-track semantic map and high-precision localization of a local map in the constructed venue map.

EPLF-VINS: Real-Time Monocular Visual-Inertial SLAM With Efficient Point-Line Flow Features

This letter introduces an efficient visual-inertial simultaneous localization and mapping (SLAM) method using point and line features. Currently, point-based SLAM methods do not perform well in scenarios such as weak textures and motion blur. Many researchers have noticed the excellent properties of line features in space and have attempted to develop line-based SLAM systems. However, the vast computational effort of the line extraction and description matching process makes it challenging to guarantee the real-time performance of the whole SLAM system, and the incorrect line detection and matching limit the performance improvement of the SLAM system. In this letter, we improve the traditional line detection model by means of short-line fusion, line feature uniform distribution, and adaptive threshold extraction to obtain high-quality line features for constructing SLAM constraints. Based on the gray level invariance assumption and colinear constraint, we propose a line optical flow tracking method, which significantly improves the speed of line feature matching. In addition, a measurement model that is independent of line endpoints is presented for estimating line residuals. The experimental results show that our algorithm improves the efficiency of line feature detection and matching and localization accuracy.

SLAM for Indoor Parking: A Comprehensive Benchmark Dataset and a Tightly Coupled Semantic Framework

For the task of autonomous indoor parking, various Visual-Inertial Simultaneous Localization And Mapping (SLAM) systems are expected to achieve comparable results with the benefit of complementary effects of visual cameras and the Inertial Measurement Units. To compare these competing SLAM systems, it is necessary to have publicly available datasets, offering an objective way to demonstrate the pros/cons of each SLAM system. However, the availability of such high-quality datasets is surprisingly limited due to the profound challenge of the groundtruth trajectory acquisition in the Global Positioning Satellite denied indoor parking environments. In this article, we establish BeVIS, a large-scale Be nchmark dataset with V isual (front-view), I nertial and S urround-view sensors for evaluating the performance of SLAM systems developed for autonomous indoor parking, which is the first of its kind where both the raw data and the groundtruth trajectories are available. In BeVIS, the groundtruth trajectories are obtained by tracking artificial landmarks scattered in the indoor parking environments, whose coordinates are recorded in a surveying manner with a high-precision Electronic Total Station. Moreover, the groundtruth trajectories are comprehensively evaluated in terms of two respects, the reprojection error and the pose volatility, respectively. Apart from BeVIS, we propose a novel tightly coupled semantic SLAM framework, namely VIS SLAM -2, leveraging V isual (front-view), I nertial, and S urround-view sensor modalities, specially for the task of autonomous indoor parking. It is the first work attempting to provide a general form to model various semantic objects on the ground. Experiments on BeVIS demonstrate the effectiveness of the proposed VIS SLAM -2. Our benchmark dataset BeVIS is publicly available at https://shaoxuan92.github.io/BeVIS .

A Resilient Method for Visual–Inertial Fusion Based on Covariance Tuning

To improve localization and pose precision of visual-inertial simultaneous localization and mapping (viSLAM) in complex scenarios, it is necessary to tune the weights of the visual and inertial inputs during sensor fusion. To this end, we propose a resilient viSLAM algorithm based on covariance tuning. During back-end optimization of the viSLAM process, the unit-weight root-mean-square error (RMSE) of the visual reprojection and IMU preintegration in each optimization is computed to construct a covariance tuning function, producing a new covariance matrix. This is used to perform another round of nonlinear optimization, effectively improving pose and localization precision without closed-loop detection. In the validation experiment, our algorithm outperformed the OKVIS, R-VIO, and VINS-Mono open-source viSLAM frameworks in pose and localization precision on the EuRoc dataset, at all difficulty levels.

DiT-SLAM: Real-Time Dense Visual-Inertial SLAM with Implicit Depth Representation and Tightly-Coupled Graph Optimization.

Recently, generating dense maps in real-time has become a hot research topic in the mobile robotics community, since dense maps can provide more informative and continuous features compared with sparse maps. Implicit depth representation (e.g., the depth code) derived from deep neural networks has been employed in the visual-only or visual-inertial simultaneous localization and mapping (SLAM) systems, which achieve promising performances on both camera motion and local dense geometry estimations from monocular images. However, the existing visual-inertial SLAM systems combined with depth codes are either built on a filter-based SLAM framework, which can only update poses and maps in a relatively small local time window, or based on a loosely-coupled framework, while the prior geometric constraints from the depth estimation network have not been employed for boosting the state estimation. To well address these drawbacks, we propose DiT-SLAM, a novel real-time Dense visual-inertial SLAM with implicit depth representation and Tightly-coupled graph optimization. Most importantly, the poses, sparse maps, and low-dimensional depth codes are optimized with the tightly-coupled graph by considering the visual, inertial, and depth residuals simultaneously. Meanwhile, we propose a light-weight monocular depth estimation and completion network, which is combined with attention mechanisms and the conditional variational auto-encoder (CVAE) to predict the uncertainty-aware dense depth maps from more low-dimensional codes. Furthermore, a robust point sampling strategy introducing the spatial distribution of 2D feature points is also proposed to provide geometric constraints in the tightly-coupled optimization, especially for textureless or featureless cases in indoor environments. We evaluate our system on open benchmarks. The proposed methods achieve better performances on both the dense depth estimation and the trajectory estimation compared to the baseline and other systems.

WHUVID: A Large-Scale Stereo-IMU Dataset for Visual-Inertial Odometry and Autonomous Driving in Chinese Urban Scenarios

In this paper, we present a challenging stereo-inertial dataset collected onboard a sports utility vehicle (SUV) for the tasks of visual-inertial odometry (VIO), simultaneous localization and mapping (SLAM), autonomous driving, object detection, and other computer vision techniques. We recorded a large set of time-synchronized stereo image sequences (2 × 1280 × 720 @ 30 fps RGB) and corresponding inertial measurement unit (IMU) readings (400 Hz) from a Stereolabs ZED2 camera, along with centimeter-level-accurate six-degree-of-freedom ground truth (100 Hz) from a u-blox GNSS-IMU navigation device with real-time kinematic correction signals. The dataset comprises 34 sequences recorded during November 2020 in Wuhan, the largest city of Central China. Further, the dataset contains abundant unique urban scenes and features of a complex modern metropolis, which have rarely appeared in previously released benchmarks. Results from milestone VIO/SLAM algorithms reveal that methods exhibiting excellent performance on established datasets such as KITTI and EuRoC perform unsatisfactorily when moved outside the laboratory to the real world. We expect our dataset to reduce this limitation by providing more challenging and diverse scenarios to the research community. The full dataset with raw and calibrated data is publicly available along with a lightweight MATLAB/Python toolbox for preprocessing and evaluation. The dataset can be downloaded in its entirety from the uniform resource locator (URL) we provide in the main text.

A New Visual Inertial Simultaneous Localization and Mapping (SLAM) Algorithm Based on Point and Line Features

In view of traditional point-line feature visual inertial simultaneous localization and mapping (SLAM) system, which has weak performance in accuracy so that it cannot be processed in real time under the condition of weak indoor texture and light and shade change, this paper proposes an inertial SLAM method based on point-line vision for indoor weak texture and illumination. Firstly, based on Bilateral Filtering, we apply the Speeded Up Robust Features (SURF) point feature extraction and Fast Nearest neighbor (FLANN) algorithms to improve the robustness of point feature extraction result. Secondly, we establish a minimum density threshold and length suppression parameter selection strategy of line feature, and take the geometric constraint line feature matching into consideration to improve the efficiency of processing line feature. And the parameters and biases of visual inertia are initialized based on maximum posterior estimation method. Finally, the simulation experiments are compared with the traditional tightly-coupled monocular visual–inertial odometry using point and line features (PL-VIO) algorithm. The simulation results demonstrate that the proposed an inertial SLAM method based on point-line vision for indoor weak texture and illumination can be effectively operated in real time, and its positioning accuracy is 22% higher on average and 40% higher in the scenario that illumination changes and blurred image.

Visual-Inertial Simultaneous Localization and Mapping: Dynamically Fused Point-Line Feature Extraction and Engineered Robotic Applications

For robots in unknown environments, the visual-inertial simultaneous localization and mapping (SLAM) incorporates the multisensor data and enables a globally consistent trajectory tracking and mapping. Given the scene structures in the SLAM problem, the high-quality lines can serve as primitives and provide additional constraints for camera pose estimation. This study is devoted to a pipeline design of dynamically fused point-line visual-inertial navigation system (VINS). The design builds on the heritage of VINS-Mono but employs an ensemble pose estimation strategy to integrate point and line features. As far as we know, it is the first optimization-based monocular VINS method that gives an insight into the quantitative relationship between point and line feature numbers. Additionally, the measured pixels for line feature-length determination and two hidden parameters in line segment detector (LSD) extractor are investigated, guaranteeing the line feature extraction optimization in both quality and efficiency. The benchmark dataset and real-world tests are conducted. In terms of challenging scene tests in the EuRoc dataset, results are compared against ROVIO, VINS-Mono, and PL-VINS frameworks, revealing our design is substantially more accurate and robust than others with both locally and globally consistent pose estimates. The engineered algorithm in the camera “Mynteye” further enables the Bulldog-CX Robot platform verification. The results concerning the drifts, time taken for line feature extraction, and real-world trajectory consistency show our design outperforms the point-based VINS. The strong adaptability of our dynamically fused point-line VINS to the broad range of indoor and outdoor scenes is simultaneously verified.

Parallel VINS-Mono algorithm based on GPUs in embedded devices

Traditional visual-inertial simultaneous localization and mapping algorithms are usually designed based on CPUs, and they cannot effectively utilize the parallel computing function of GPUs if they are directly transplanted to an embedded board with a GPU module. However, the computing power of embedded devices is limited. It is unreasonable for the visual-inertial simultaneous localization and mapping algorithm to occupy most CPU computing resources while the GPU is idle. In this article, a parallelization scheme for the VINS-Mono algorithm based on GPU parallel computing technology is proposed. Based on the compute unified device architecture, the construction and solution of the incremental equation are parallelized in the nonlinear optimization process of the algorithm, and the parallelization methods provided by cuSOLVER and cuBLAS are used to carry out the marginalization of the algorithm. In addition, the program for the detection and matching of image feature points in the process of optical flow tracking is rewritten in the algorithm to realize the parallelization of optical flow tracking. After parallelization, the algorithm is found to run well on a heterogeneous computing model composed of a CPU and GPU and can fully exploit the parallel computing power of the GPU. The proposed method was tested on an NVIDIA’s Jetson TX2 module and compared with the VINS-Mono algorithm; the speeds of the construction and solution of the incremental equation were found to be the same, but the optical flow tracking and marginalization speed of the proposed scheme exhibited improvements of about 1.5–1.7 times and 1.9 times, respectively.

An Improved Initialization Method for Monocular Visual-Inertial SLAM

In the aim of improving the positioning accuracy of the monocular visual-inertial simultaneous localization and mapping (VI-SLAM) system, an improved initialization method with faster convergence is proposed. This approach is classified into three parts: Firstly, in the initial stage, the pure vision measurement model of ORB-SLAM is employed to make all the variables visible. Secondly, the frequency of the IMU and camera was aligned by IMU pre-integration technology. Thirdly, an improved iterative method is put forward for estimating the initial parameters of IMU faster. The estimation of IMU initial parameters is divided into several simpler sub-problems, containing direction refinement gravity estimation, gyroscope deviation estimation, accelerometer bias, and scale estimation. The experimental results on the self-built robot platform show that our method can up-regulate the initialization convergence speed, simultaneously improve the positioning accuracy of the entire VI-SLAM system.

Kimera: From SLAM to spatial perception with 3D dynamic scene graphs

Humans are able to form a complex mental model of the environment they move in. This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e.g., objects, rooms, buildings), includes static and dynamic entities and their relations (e.g., a person is in a room at a given time). In contrast, current robots’ internal representations still provide a partial and fragmented understanding of the environment, either in the form of a sparse or dense set of geometric primitives (e.g., points, lines, planes, and voxels), or as a collection of objects. This article attempts to reduce the gap between robot and human perception by introducing a novel representation, a 3D dynamic scene graph (DSG), that seamlessly captures metric and semantic aspects of a dynamic environment. A DSG is a layered graph where nodes represent spatial concepts at different levels of abstraction, and edges represent spatiotemporal relations among nodes. Our second contribution is Kimera, the first fully automatic method to build a DSG from visual–inertial data. Kimera includes accurate algorithms for visual–inertial simultaneous localization and mapping (SLAM), metric–semantic 3D reconstruction, object localization, human pose and shape estimation, and scene parsing. Our third contribution is a comprehensive evaluation of Kimera in real-life datasets and photo-realistic simulations, including a newly released dataset, uHumans2, which simulates a collection of crowded indoor and outdoor scenes. Our evaluation shows that Kimera achieves competitive performance in visual–inertial SLAM, estimates an accurate 3D metric–semantic mesh model in real-time, and builds a DSG of a complex indoor environment with tens of objects and humans in minutes. Our final contribution is to showcase how to use a DSG for real-time hierarchical semantic path-planning. The core modules in Kimera have been released open source.

An Enhanced Pedestrian Visual-Inertial SLAM System Aided with Vanishing Point in Indoor Environments.

The visual-inertial simultaneous localization and mapping (SLAM) is a feasible indoor positioning system that combines the visual SLAM with inertial navigation. There are accumulated drift errors in inertial navigation due to the state propagation and the bias of the inertial measurement unit (IMU) sensor. The visual-inertial SLAM can correct the drift errors via loop detection and local pose optimization. However, if the trajectory is not a closed loop, the drift error might not be significantly reduced. This paper presents a novel pedestrian dead reckoning (PDR)-aided visual-inertial SLAM, taking advantage of the enhanced vanishing point (VP) observation. The VP is integrated into the visual-inertial SLAM as an external observation without drift error to correct the system drift error. Additionally, the estimated trajectory’s scale is affected by the IMU measurement errors in visual-inertial SLAM. Pedestrian dead reckoning (PDR) velocity is employed to constrain the double integration result of acceleration measurement from the IMU. Furthermore, to enhance the proposed system’s robustness and the positioning accuracy, the local optimization based on the sliding window and the global optimization based on the segmentation window are proposed. A series of experiments are conducted using the public ADVIO dataset and a self-collected dataset to compare the proposed system with the visual-inertial SLAM. Finally, the results demonstrate that the proposed optimization method can effectively correct the accumulated drift error in the proposed visual-inertial SLAM system.

Position detection for electric vehicle DWCS using VI-SLAM method

The dynamic wireless charging system (DWCS) is developed to solve the problems of large battery volume and mileage anxiety of electric vehicles. However, the accurate position detection for electric vehicle DWCS is facing challenge. The traditional communication, detection and estimation methods are difficult to accurately obtain the position. To tackle this problem, the visual inertial simultaneous localization and mapping (VI-SLAM) method is applied to the electric vehicles DWCS. Firstly, the graph optimization based tight coupling method is used to integrate the monocular visual and inertial measurement unit (IMU) measurements. Secondly, the NVIDIA TX2 and MYNT VI-sensor suite are assembled, which the MTi300-IMU is treated as the ground truth system. Finally, the mobile vehicle is controlled to race on the simulated DWCS pathway. The experimental result shows that the method achieves great performance with the accuracy of centimeter level. In particular, the root mean square error (RMSE) of pose (i.e. position and orientation) in X, Y, Z directions are 0.086 m, 0.092 m, 0.102 m and 2.423°, 1.682°, 2.501°, respectively. Compared with the smooth variable structure filter (SVSF) based SLAM method, 0.106 m, 0.098 m, 0.130 m and 3.069°, 3.261°, 2.961°, the accuracy of ours is increased by 18.87%, 6.12%, 21.54% and 21.05%, 45.42%, 15.54%, respectively.

High-Precision Extrinsic Calibration Method of a Time-of-Flight IMU RGB-Camera With Loop Closure Constraints

Accurate localization is key to realizing an autonomous mobile robot. The fusion of depth data measured by a time-of-flight (TOF) camera with visual-inertial simultaneous localization and mapping (SLAM) can greatly improve depth estimation and mapping. To achieve a robust sensor fusion system, precise calibration of one sensor to another is essential. In this paper, we propose a joint extrinsic calibration method for a TOF- IMU RGB-camera system. Further, we design a pattern that consists of white circles with high reflectivity, and a black background with high absorptivity. Using our calibration method, circular features can be accurately extracted from amplitude images as well as from RGB images. We correct the TOF depth measurement errors, which depend on the pixel location and distance of the pixel away from the object, by establishing an error distribution model with a B-spline function. All sensors are fixed on a platform, and the position of each sensor relative to the other sensors is unchanged, forming loop closure constraints. Based on this prerequisite, we utilize the loop closure constraints between multiple sensors to obtain more accurate extrinsic parameters. By taking the loop closure constraints into account, the stability and robustness of the calibration is increased. The experimental results validate the proposed method and prove its accuracy. Thus, we achieve high precision in multi-sensor extrinsic calibration, which is the necessary foundation of SLAM research.