Object detection is an important aspect for autonomous driving vehicles (ADV), which may comprise of a machine learning model that detects a range of classes. As the deployment of ADV widens globally, the variety of objects to be detected may increase beyond the designated range of classes. Continual learning for object detection essentially ensure a robust adaptation of a model to detect additional classes on the fly. This study proposes a novel continual learning method for object detection that learns new object class(es) along with cumulative memory of classes from prior learning rounds to avoid any catastrophic forgetting. The results of PASCAL VOC 2007 have suggested that the proposed ER method obtains 4.3% of mAP drop compared against the all-classes learning, which is the lowest amongst other prior arts.

Loop-closure detection plays a critical role in simultaneous localization and mapping (SLAM) systems. The primary task of loop-closure detection involves analyzing previously visited locations and correcting mapping errors, which typically stem from intrinsic noise in sensor data and accumulate over time. However, the burden of storing and querying/searching for previously visited information continues to increase with time. Consequently, reducing the amount of data stored becomes increasingly important. In this study, we propose a trainable hypervectors (THV) encoder, integrating quantization and a lookup table (LUT) to significantly enhance execution speed. Additionally, we employ a triangular mask in second-order pooling (SOP) for filtering extraneous features in the encoder and introduce binary quadruplet loss to efficiently train binary feature representations. We evaluate our method extensively on the KITTI, MulRan and Wild-Places datasets. The experiments demonstrate that our method substantially improves efficiency while maintaining accuracy. Moreover, our method effectively utilizes the 3D-NAND flash in-memory computing technique to improve execution performance.