This paper develops a real-time localization framework based on multi-sensor fusion for the autonomous navigation of mobile robots in challenging off-road environments. The proposed framework employs a hybrid coupling strategy at its front-end, which integrates Inertial Measurement Unit (IMU) data with Global Navigation Satellite System (GNSS) and LiDAR. As a consequence, GNSS Inertial Odometry (GIO) and Lidar Inertial Odometry (LIO) are created and these are crucial for achieving precise localization in real-time. The RGB-Height-Intensity descriptor is used to improve the precision of loop closure detection while maintaining search efficiency. This is achieved by integrating surface reflectance data from the LiDAR intensity channel and RGB data from camera images. Finally, in the back-end procedure of this framework, the accuracy of posture estimation is enhanced by the optimization of odometry and loop closure factors. Additionally, a framework of real-time localization and autonomous navigation is developed and implemented on a wheeled robot to verify the proposed real-time localization framework. The experimental results indicate the framework exhibited higher performance in off-road environments, where conventional localization techniques generally degrade.

To perform collaborative tasks in unknown environment, multirobot simultaneous localization and mapping (SLAM) must establish their spatial constraints from the perception data, and locate each robot within a unified and consistent shared environmental map. However, it faces the challenges of insufficient utilization of perception information, low robustness, and poor real-time communication. For example, most current work focuses on historical loop constraints, neglecting the more direct, geometrically stronger, and communication-efficient “real-time intersection” constraints. To overcome this problem, we propose a distributed robust communication-efficient multirobot SLAM framework, integrating real-time intersection constraints and historical loop constraints. The detection and tracking results of anchor robot are used as real-time intersection constraints to update the front-end status estimation, and the historical loop constraints within and between robots are considered to establish a distributed optimization back-end. Additionally, to generate accurate real-time intersection constraints, a two-stage clustering detection and tracking method for teammate robots based on reflective points is proposed. We established an experimental platform consisting of different mobile robots and collected datasets to test the system. These experimental results in various scenes show that combining real-time intersection and historical loop constraints can effectively improve robust positioning, global consistent mapping and efficient communication performance of multirobot SLAM systems.

An efficient policy allocation algorithm for the transmission of embedded bit streams over noisy channels with feedback is proposed. The transmission is based on the type-II Hybrid ARQ/FEC protocol and uses a nested sequence C of channel codes to protect the packets. There are also constraints on the total bit budget and on the allowed number of retransmissions per packet. The allocation algorithm assigns different protection policies, each policy being a subset of C, to different packets to maximize the average number of correctly received source bits. We study the performance and the complexity of the proposed scheme through the transmission of images encoded by JPEG2000 over mobile channels with correlated Rayleigh fading. We demonstrate by simulations that the proposed multiple-policy scheme provides significant improvements over a purely FEC scheme with no feedback and also the existing fixed-policy (equal error protection) schemes. Our results show that feedback is particularly helpful for poor channel conditions and that the proposed scheme is very robust against changes in the channel signal-to-noise ratio (SNR) and the mobile speed.

An efficient policy allocation algorithm for the transmission of embedded bit streams over noisy channels with feedback is proposed. The transmission is based on the type-II hybrid ARQ/FEC protocol and uses a nested sequence C of channel codes to protect the packets. There are also constraints on the total bit budget and on the allowed number of retransmissions per packet. The allocation algorithm assigns different protection policies, each policy being a subset of C, to different packets to maximize the average number of correctly received source bits. We study the performance and the complexity of the proposed scheme through the transmission of images encoded by JPEG2000 over mobile channels with correlated Rayleigh fading. We demonstrate by simulations that the proposed multiple-policy scheme provides significant improvements over a purely FEC scheme with no feedback and also the existing fixed-policy schemes. Our results show that feedback is particularly helpful for poor channel conditions and that the proposed scheme is very robust against changes in the channel signal-to-noise ratio (SNR) and the mobile speed.