Near Field iToF LIDAR Depth Improvement from Limited Number of Shots

Indirect Time of Flight LiDARs can indirectly calculate the scene's depth from the phase shift angle between transmitted and received laser signals with amplitudes modulated at a predefined frequency. Unfortunately, this method generates ambiguity in calculated depth when the phase shift angle value exceeds $2\pi$. Current state-of-the-art methods use raw samples generated using two distinct modulation frequencies to overcome this ambiguity problem. However, this comes at the cost of increasing laser components' stress and raising their temperature, which reduces their lifetime and increases power consumption. In our work, we study two different methods to recover the entire depth range of the LiDAR using fewer raw data sample shots from a single modulation frequency with the support of sensor's gray scale output to reduce the laser components' stress and power consumption.

Collision-Free Navigation using Evolutionary Symmetrical Neural Networks

Collision avoidance systems play a vital role in reducing the number of vehicle accidents and saving human lives. This paper extends the previous work using evolutionary neural networks for reactive collision avoidance. We are proposing a new method we have called symmetric neural networks. The method improves the model's performance by enforcing constraints between the network weights which reduces the model optimization search space and hence, learns more accurate control of the vehicle steering for improved maneuvering. The training and validation processes are carried out using a simulation environment - the codebase is publicly available. Extensive experiments are conducted to analyze the proposed method and evaluate its performance. The method is tested in several simulated driving scenarios. In addition, we have analyzed the effect of the rangefinder sensor resolution and noise on the overall goal of reactive collision avoidance. Finally, we have tested the generalization of the proposed method. The results are encouraging; the proposed method has improved the model's learning curve for training scenarios and generalization to the new test scenarios. Using constrained weights has significantly improved the number of generations required for the Genetic Algorithm optimization.