Inverse k-visibility for RSSI-based Indoor Geometric Mapping

In recent years, the increased availability of WiFi in indoor environments has gained an interest in the robotics community to leverage WiFi signals for enhancing indoor SLAM (Simultaneous Localization and Mapping) systems. SLAM technology is widely used, especially for the navigation and control of autonomous robots. This paper discusses various works in developing WiFi-based localization and challenges in achieving high-accuracy geometric maps. This paper introduces the concept of inverse k-visibility developed from the k-visibility algorithm to identify the free space in an unknown environment for planning, navigation, and obstacle avoidance. Comprehensive experiments, including those utilizing single and multiple RSSI signals, were conducted in both simulated and real-world environments to demonstrate the robustness of the proposed algorithm. Additionally, a detailed analysis comparing the resulting maps with ground-truth Lidar-based maps is provided to highlight the algorithm's accuracy and reliability.

Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO)

Focal-Plane Sensor-Processor Arrays (FPSP)s are an emerging technology that can execute vision algorithms directly on the image sensor. Unlike conventional cameras, FPSPs perform computation on the image plane -- at individual pixels -- enabling high frame rate image processing while consuming low power, making them ideal for mobile robotics. FPSPs, such as the SCAMP-5, use parallel processing and are based on the Single Instruction Multiple Data (SIMD) paradigm. In this paper, we present BIT-VIO, the first Visual Inertial Odometry (VIO) which utilises SCAMP-5.BIT-VIO is a loosely-coupled iterated Extended Kalman Filter (iEKF) which fuses together the visual odometry running fast at 300 FPS with predictions from 400 Hz IMU measurements to provide accurate and smooth trajectories.